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The Application of Machine Learning
Tools on Complex and Big Data Projects
David J Patrishkoff
2
LSS Track #10
Session #LSS-103
The Application of Machine Learning
Tools on Complex and Big Data Projects
9:15 AM – 9:50 AM
March 14th, 2019
LSS WORLD CONFERENCE
David J Patrishkoff
3
INTRODUCTION
Each of the Google search terms “Machine
Learning” (ML), and “Artificial Intelligence” (AI) are
now more popular in the USA than “Six Sigma”
and “Lean Manufacturing” combined. ML is a
branch of AI.
The advantages of ML analysis applications for
complex problem-solving, data mining, and
research projects will be presented.
4
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Six Sigma
Artificial Intelligence
Machine Learning
Lean Manufacturing
GOOGLE SEARCH TERM POPULARITY
IN THE USA - 2014 TO 2019
Source: Google Trends
5
LEARNING POINT TOPICS
1. LSS analysis tools -
upgrades available
2. Overcoming
superficial analysis &
analysis-paralysis
risks
3. ML applications for
complex and big data
projects
1. LSS ANALYSIS TOOLS –
UPGRADES AVAILABLE
7
The greatest obstacle to
discovery is not ignorance
– It is the illusion of
knowledge
Daniel J Boorstin
Scholar
When you apply computer science and
machine learning to areas that haven't
had any innovation in 50 years, you
can make rapid advances that seem
really incredible
Bill Maris
Founder and first CEO of Google Ventures
The biggest room in the
world is the room for
improvement
Helmut Schmidt
German Politician
NEVER STOP IMPROVING
8
LSS ANALYSIS UPGRADES ARE
AVAILABLE
• Tech upgrades often
come faster than
expected
• Continually assess
new options to be
more effective
• Be a life-long learner
• Don’t become
obsoleted, one
upgrade at a time…
WARNING!
You have ignored
35,476 Upgrades
9
SIGNS THAT YOUR DATA COMPLEXITY HAS
OUTGROWN YOUR SOFTWARE CAPABILITY
• Analysis software often
freezes & crashes
• Data stratification limits
are impeding your analysis
• Unrestricted non-linear
analysis is needed
• You need to find hidden
pockets of factor
interactions
• Project complexity is
driving circular analysis
• You cannot find the sweet
spot between simplicity
and analysis paralysis
2. OVERCOMING
SUPERFICIAL ANALYSIS &
ANALYSIS-PARALYSIS
RISKS
11
DRIVERS OF SUPERFICIAL ANALYSIS
Shallow exploration efforts
Time constraintsUnderestimating complexity
12
OTHER SUPERFICIAL ANALYSIS RISKS
• Shortcuts = Analysis,
professional & reputational
risks
• Just exploring the blindingly
obvious
• Using weak analysis tools
to address a complex
problem
• Substituting process &
methods with instincts and
impulsiveness
• Applying random or
inefficient strategies for
analysis
Only strong
characters can
resist the
temptation of
superficial
analysis.
Albert Einstein
13
ADDRESSING COMPLEXITY WITH KISS
K e e p
I t
S u p e r
S i m p l e
• KISS is the ultimate
goal for data
analysis
• KISS makes
everyone happy
• KI$$ reduces
analysis time and
costs
• KISS is tough to
implement
14
KISS ANALYSIS SHOULD INCLUDE ASPECTS
OF ERROR-PROOFING
• Error-proofed software
analysis should include:
• Ease of use
• Intuitive operations
• Analysis wizard support
• Video instruction
availability
• Easy access to experts
and help desks
• Checklists for sequential
analysis steps
• Automated & exhaustive
charting of all data
• Automated analysis
interpretation comments
15
Superficial
Analysis
Analysis
Paralysis
An Effective
Data Analysis Strategy
Shallow analysis Circular analysis
CREATING AN EFFECTIVE
STRATEGY OF ANALYSIS (SOA)
a written analysis
strategy with
sequentially listed
analysis tasks
16
THE PURSUIT OF
ANALYSIS WITHOUT PARALYSIS
• Analysis without a plan
can lead to paralysis.
• Don’t get so
consumed with solving
a problem that you
forget to solve the
problem
• Machine Learning and
Neural Network analysis
software are quite
simple… but it just takes
a genius to understand
their simplicity
The only simplicity to be trusted is
the simplicity to be found on the far
side of complexity
Alfred North Whitehead
(English mathematician and philosopher)
3. ML APPLICATIONS FOR
COMPLEX AND BIG DATA
PROJECTS
18
WHEN YOUR PROJECT DATA OUTGROWS
YOUR ANALYSIS SOFTWARE CAPABILITY
Options:
• Narrow the project
scope
• Limit the number of
factors in the study
• Transfer the analysis to
64 Bit / high data
capability software
• Explore the capabilities
of high data capability
machine learning
software
• Write code for your own
analysis
19
Small
(100s - millions)
Logscale
for project
complexity

Project data description
(from a Big Data perspective)
Classic
LSS
problem-
solving
Big Data
analysis
and AI
solutions
THE EVOLUTION OF BUSINESS DATA AND
INFORMATION ANALYSIS

Big Data (1TB++)
(trillions++ of data fields)
Analysis Capability Learning
Opportunity for LSS Belts
Medium
(billions)


Data Scientists, data miners,
social media miners,
quants, AI engineers, etc.
WBs, YBs, GBs,
BBs & MBBs






20
SOME OF THE ANALYSIS CAPABILITIES
AVAILABLE FOR COMPLEX LSS PROJECTS
• Advanced statistical analysis
• Automated non-linear analysis
• Regression and classification trees
with hotspot detection
• Model factor importance rankings
• Adjustment for confounding factors
• Automated charting of all
possible 2D & 3D plots
64 Bit SW
Predictive Modeling
Data Mining / Research
Problem-Solving
• Supervised & unsupervised analysis modes
32 Bit SW
• Automated data binning suggestions
• 64 bit large data handling capability
• Predictive Modeling
21
SCOPE OF ANALYSIS FOR MY
TRANSPORTATION SAFETY RESEARCH
• 1975 to 2017 CY traffic
accidents that involved at
least 1 fatality in a vehicle
• Data available for all
survivors and victims
• Analyzed over 300 scientific
accident research articles
• Over 1.3 billion fields of
data available in US FARS
data (Fatality Analysis
Reporting System)
• Discover why 18 other high
income countries have
lower traffic fatality rates
than the USA
Two of the many road-side
shrines for vehicle accident
victims
22
SOFTWARE SUPPORTING MY
TRANSPORTATION SAFETY RESEARCH
from Minitab
Machine learning software
From Oakdale Engineering
Non-linear Multiple
Variable Regression
Research Project Mind
Mapping and Planning
Also offers R-Sq
& P-Values for
non-linear 3D
regression
surfaces
Statistical analysis software
23
MY TRANSPORTATION SAFETY
RESEARCH GOAL
Discover the interacting factors that result in non-injuries, injury, or
death in serious vehicle crashes
24
CRASHWORTHINESS: THE VEHICLES ABILITY TO
PROTECT ITS OCCUPANTS IN ANY ACCIDENT
Some standard impact types:
Frontal impact
Side or angle impact
Tree or pole impact
Rear impact Rollovers
25
1975 / 2018 MY
Chevy Impala
1975 / 2018 MY
Ford Pickup Truck
1975 / 2018 MY
Chrysler Van
1975 / 2018 MY
Jeep Cherokee
RESEARCH SCOPE: ALL PASSENGER VEHICLE
FATAL ACCIDENTS FROM 1975 TO PRESENT
26
RESEARCH QUESTION: HOW FAST AND BY
HOW MUCH DOES A NHTSA STAR FADE?
NHTSA (National Highway Traffic Safety Administration)
NHTSA’s 5-Star Vehicle Safety Ratings Program
Ratings standards have changed and expanded
over the years
Vehicle structures weaken over time due to material fatigue and
corrosion which lessons their ability to absorb energy during a
crash
27
HISTORICAL TREND: THE AGING USA
VEHICLE FLEET ON THE ROADS
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
13
12
11
10
9
8
7
6
5
4
Year of Accident
MeanofAgeofVehicle
4
7
11
13 Passenger Cars
Pickups
Utility Vehicles
Vans
Vehicle Body Type
Age of Passenger Vehicles on US Roads
28
HISTORICAL TREND: SEAT BELT USAGE
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
90
80
70
60
50
40
30
20
10
0
Year of Accident
MeanofPercentSeatBeltUsage
0
80
70
10
Passenger Cars
Pickups
Utility Vehicles
Vans
Vehicle Body Type
% Seat Belt Usage by Passenger Vehicle Type
29
IDENTIFYING THE OPTIMAL BIN SIZE AND
CUT-OFF VALUES
SPM can identify the ideal min and max bin values for continuous
values that will be statistically analyzed in stratified groupings.
Example: Age of Vehicle.
30
20+191817161514131211109876543210
55
50
45
40
35
30
25
20
Age of Vehicle
PercentKilled
23.6769
27.0855
34.661
35.6656
36.656636.6186
42.80943.051
45.3231
49.6786
50.8082
49.2294
26.2783
52.9058
27.698
28.835728.2635
29.8149
32.5149
30.7062
34.1622
Interval Plot of Percent Killed vs Age of Vehicle
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
for belted drivers of passenger cars - 1978 - 2017 CY
NON-OPTIMIZED BINNING OF VEHICLE AGE
n = 79,082
31
OPTIMIZED BINNING OF VEHICLE AGE
16 to 20+13 to 1511 to 129 to 107 to 865430 to 2
50
45
40
35
30
25
10 age of Vehicle Groups
PercentKilled
26.5412
27.698
28.8357 28.2635
29.8149
31.611
34.4141
36.1481
40.4395
50.0487
Interval Plot of Percent Killed vs 10 age of Vehicle Groups
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
for belted drivers of passenger cars - 1978 - 2017 CY
n = 79,082
The optimized binning group
sizes resulted in groups with
more consistent confidence
interval ranges
32
75+65 - 7455 - 6445 - 5435 - 4425 - 3421 - 2416 - 2012 - 150 - 11
200
150
100
50
0
-50
-100
Occupant Age Groups
PercentKilled
62.741
46.609639.716834.554730.204127.429528.106127.6687
25.7576
33.3333
Interval Plot of Percent Killed
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
for belted drivers of passenger cars - 1978 to 2017 CY
NON-OPTIMIZED BINNING OF
OCCUPANT AGES
33
OPTIMIZED BINNING OF OCCUPANT AGES
72++59 to 7149 to 5841 to 4835 to 4030 to 3426 to 2922 to 2519 to 210 to 18
65
60
55
50
45
40
35
30
25
10 Occupant Age Groups
PercentKilled
60.2062
42.8872
36.7335
32.1771
29.199728.428
26.771227.646627.7219
27.725
Interval Plot of Percent Killed
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
for belted drivers of passenger cars - 1978 to 2017 CY
The optimized binning group
sizes resulted in groups with
more consistent confidence
interval ranges
34
NON-OPTIMIZED BINNING OF
VEHICLE WEIGHT
4701+3951 - 47003201 - 39502451 - 3200Up to 2450
50
40
30
20
10
0
Vehicle Weight Group
PercentKilled
9.04762
20.9455
26.662
35.6063
45.6874
Interval Plot of Percent Killed
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
for belted drivers of passenger cars - 1978 to 2017 CY
35
OPTIMIZED BINNING OF VEHICLE WEIGHT
3778++
3455
to
3777
3261 to
3454
3110
to
3260
2951 to
3109
2723
to
2950
2553
to
2722
2404
to
2552
2181 to
2403
Under 2180
50
45
40
35
30
25
20
10 Vehicle Weight Groups
PercentKilled
20.852
25.5846
27.7143
31.3654
30.5886
35.3399
39.4967
41.521
43.4374
48.994
Interval Plot of Percent Killed
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
for belted drivers of passenger cars - 1978 to 2017
Optimized binning
= more consistent
confidence interval
ranges
36
SCOPE: 160 CRASHWORTHINESS STUDIES
INTO WHO SURVIVES SERIOUS ACCIDENTS
Selected Machine learning
analysis outputs will be shown
for 1 of 160 studies:
Driver safety for frontal
impacts in passenger cars
Machine learning analysis can
conduct all 160 studies at the
same time and automatically
sort out the safest and riskiest
occupant conditions for every
significant stratification groups
37
Lower Fatalities
(Dark blue boxes)
Higher Fatalities
(Dark red boxes)
CART TREE TOPOGRAPHY: TOP-LEVEL
COLOR CODED SPLITTING RESULTS
Smaller than optimal-sized tree split example - 90 nodes
38
Lower Fatalities
(Dark blue boxes)
Higher Fatalities
(Dark red boxes)
76.9% Fatalities: No SB
worn, > 1990 accident,
no extrication, all ages
except 12-44 YO,
n = 44,412
82.6% Fatalities:
No SB worn +
extrication + 36-
55mph speed,
n = 39,406
CART TREE TOPOGRAPHY: TOP-LEVEL
COLOR CODED SPLITTING RESULTS
39
Lower Fatalities
(Dark blue boxes)
Higher Fatalities
(Dark red boxes)
92.8% Non-Fatalities: SB worn,
no extrication, <36 mph speed,
< 65 years of occupant age
N=25,854
CART TREE TOPOGRAPHY: TOP-LEVEL
COLOR CODED SPLITTING RESULTS
40
Lower Fatalities
(Dark blue boxes)
Higher Fatalities
(Dark red boxes)
84.8% Non-Fatalities: SB worn, no extrication,
<15 mph speed, all occ ages except 12-64
YO, vehicle weight >3200 lbs,
n=730
CART TREE TOPOGRAPHY: TOP-LEVEL
COLOR CODED SPLITTING RESULTS
41
OCCUPANT_AGE_GROUPS$
TRAVEL_SPEED_GROUP$
YEAR_OF_ACCIDENT
CAR_COMPANY_HQ_REGION$
VEHICLE_WEIGHT_GROUP$
OCCUPANT_AGE_GROUPS$
OCCUPANT_AGE_GROUPS$
YEAR_OF_ACCIDENT
OCCUPANT_AGE_GROUPS$
TRAVEL_SPEED_GROUP$
TRAVEL_SPEED_GROUP$
VEHICLE_WEIGHT_GROUP$
YEAR_OF_ACCIDENT
YEAR_OF_ACCIDENT
AGE_OF_VEHICLE
YEAR_OF_ACCIDENT
MODEL_YEAR_OF_VEHICLE
OCCUPANT_AGE_GROUPS$
OCCUPANT_AGE_GROUPS$
YEAR_OF_ACCIDENT
VEHICLE_WEIGHT_GROUP$
CAR_COMPANY_HQ_REGION$
TRAVEL_SPEED_GROUP$
OCCUPANT_AGE_GROUPS$
YEAR_OF_ACCIDENT
CAR_COMPANY_HQ_REGION$
TRAVEL_SPEED_GROUP$
OCCUPANT_AGE_GROUPS$
YEAR_OF_ACCIDENT
MODEL_YEAR_OF_VEHICLE
CAR_COMPANY_HQ_REGION$
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
MODEL_YEAR_OF_VEHICLE
YEAR_OF_ACCIDENT
MODEL_YEAR_OF_VEHICLE
VEHICLE_WEIGHT_GROUP$
YEAR_OF_ACCIDENT
OCCUPANT_AGE_GROUPS$
TRAVEL_SPEED_GROUP$
YEAR_OF_ACCIDENT
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
OCCUPANT_AGE_GROUPS$
EXTRICATED_Y_N$
TRAVEL_SPEED_GROUP$
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
AGE_OF_VEHICLE
CAR_COMPANY_HQ_REGION$
TRAVEL_SPEED_GROUP$
SEX$
YEAR_OF_ACCIDENT
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
TRAVEL_SPEED_GROUP$
MODEL_YEAR_OF_VEHICLE
TRAVEL_SPEED_GROUP$ AGE_OF_VEHICLE
SEX$
YEAR_OF_ACCIDENT
AGE_OF_VEHICLE
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
VEHICLE_WEIGHT_GROUP$
OCCUPANT_AGE_GROUPS$
AGE_OF_VEHICLE
AGE_OF_VEHICLE
YEAR_OF_ACCIDENT
YEAR_OF_ACCIDENT
MODEL_YEAR_OF_VEHICLE
MODEL_YEAR_OF_VEHICLE
OCCUPANT_AGE_GROUPS$ OCCUPANT_AGE_GROUPS$
AGE_OF_VEHICLE
OCCUPANT_AGE_GROUPS$
OCCUPANT_AGE_GROUPS$
AGE_OF_VEHICLE
CAR_COMPANY_HQ_REGION$
MODEL_YEAR_OF_VEHICLE
YEAR_OF_ACCIDENT
OCCUPANT_AGE_GROUPS$
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
VEHICLE_WEIGHT_GROUP$
TRAVEL_SPEED_GROUP$
OCCUPANT_AGE_GROUPS$
EXTRICATED_Y_N$
SB_USED_Y_N$
CART TREE TOPOGRAPHY: TOP-LEVEL
SPLITTER NAME VIEW
42
EXTRICATED_Y_N$ = (Yes)
Terminal
Node 1
Class = Yes
Class Cases %
No 6866 17.4
Yes 32540 82.6
W = 39406.000
N = 39406
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 2
Class = Yes
Class Cases %
No 3922 32.6
Yes 8113 67.4
W = 12035.000
N = 12035
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Terminal
Node 3
Class = Yes
Class Cases %
No 33 28.2
Yes 84 71.8
W = 117.000
N = 117
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Terminal
Node 4
Class = No
Class Cases %
No 189 63.2
Yes 110 36.8
W = 299.000
N = 299
TRAVEL_SPEED_GROUP$ = (0 to 15)
Node 7
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(0 to 11,75+)
Class Cases %
No 222 53.4
Yes 194 46.6
W = 416.000
N = 416
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 6
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 4144 33.3
Yes 8307 66.7
W = 12451.000
N = 12451
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 5
Class = Yes
Class Cases %
No 102 28.3
Yes 258 71.7
W = 360.000
N = 360
OCCUPANT_AGE_GROUPS$ = (65 to 74,...)
Terminal
Node 6
Class = Yes
Class Cases %
No 1118 39.8
Yes 1692 60.2
W = 2810.000
N = 2810
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Terminal
Node 7
Class = Yes
Class Cases %
No 1685 48.6
Yes 1779 51.4
W = 3464.000
N = 3464
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 8
Class = Yes
Class Cases %
No 1 6.7
Yes 14 93.3
W = 15.000
N = 15
YEAR_OF_ACCIDENT <= 1984.50
Terminal
Node 9
Class = No
Class Cases %
No 866 60.2
Yes 573 39.8
W = 1439.000
N = 1439
YEAR_OF_ACCIDENT > 1984.50
Terminal
Node 10
Class = Yes
Class Cases %
No 309 52.8
Yes 276 47.2
W = 585.000
N = 585
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 13
Class = No
YEAR_OF_ACCIDENT <= 1984.50
Class Cases %
No 1175 58.1
Yes 849 41.9
W = 2024.000
N = 2024
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Node 12
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 1176 57.7
Yes 863 42.3
W = 2039.000
N = 2039
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 11
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 2861 52.0
Yes 2642 48.0
W = 5503.000
N = 5503
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 10
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(65 to 74,75+)
Class Cases %
No 3979 47.9
Yes 4334 52.1
W = 8313.000
N = 8313
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Terminal
Node 11
Class = Yes
Class Cases %
No 73 43.2
Yes 96 56.8
W = 169.000
N = 169
YEAR_OF_ACCIDENT <= 1987.50
Terminal
Node 12
Class = No
Class Cases %
No 452 72.2
Yes 174 27.8
W = 626.000
N = 626
OCCUPANT_AGE_GROUPS$ = (55 to 64,...)
Terminal
Node 13
Class = Yes
Class Cases %
No 32 38.1
Yes 52 61.9
W = 84.000
N = 84
OCCUPANT_AGE_GROUPS$ = (45 to 54)
Terminal
Node 14
Class = No
Class Cases %
No 43 71.7
Yes 17 28.3
W = 60.000
N = 60
YEAR_OF_ACCIDENT > 1987.50
Node 16
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(55 to 64,65 to 74)
Class Cases %
No 75 52.1
Yes 69 47.9
W = 144.000
N = 144
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 15
Class = No
YEAR_OF_ACCIDENT <= 1987.50
Class Cases %
No 527 68.4
Yes 243 31.6
W = 770.000
N = 770
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 14
Class = No
OCCUPANT_AGE_GROUPS$ =
(0 to 11,75+)
Class Cases %
No 600 63.9
Yes 339 36.1
W = 939.000
N = 939
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 9
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 4579 49.5
Yes 4673 50.5
W = 9252.000
N = 9252
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 8
Class = Yes
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 4681 48.7
Yes 4931 51.3
W = 9612.000
N = 9612
YEAR_OF_ACCIDENT <= 1989.50
Node 5
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 8825 40.0
Yes 13238 60.0
W = 22063.000
N = 22063
YEAR_OF_ACCIDENT > 1989.50
Terminal
Node 15
Class = Yes
Class Cases %
No 10251 23.1
Yes 34161 76.9
W = 44412.000
N = 44412
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 4
Class = Yes
YEAR_OF_ACCIDENT <= 1989.50
Class Cases %
No 19076 28.7
Yes 47399 71.3
W = 66475.000
N = 66475
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 16
Class = Yes
Class Cases %
No 9939 37.9
Yes 16262 62.1
W = 26201.000
N = 26201
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Terminal
Node 17
Class = Yes
Class Cases %
No 8559 36.2
Yes 15094 63.8
W = 23653.000
N = 23653
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Terminal
Node 18
Class = Yes
Class Cases %
No 3799 35.6
Yes 6862 64.4
W = 10661.000
N = 10661
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Terminal
Node 19
Class = Yes
Class Cases %
No 4842 47.4
Yes 5377 52.6
W = 10219.000
N = 10219
YEAR_OF_ACCIDENT <= 1985.50
Terminal
Node 20
Class = Yes
Class Cases %
No 1561 52.6
Yes 1409 47.4
W = 2970.000
N = 2970
YEAR_OF_ACCIDENT > 1985.50
Terminal
Node 21
Class = No
Class Cases %
No 3905 57.4
Yes 2898 42.6
W = 6803.000
N = 6803
AGE_OF_VEHICLE <= 5.50
Node 26
Class = Yes
YEAR_OF_ACCIDENT <= 1985.50
Class Cases %
No 5466 55.9
Yes 4307 44.1
W = 9773.000
N = 9773
AGE_OF_VEHICLE > 5.50
Terminal
Node 22
Class = Yes
Class Cases %
No 6862 52.9
Yes 6119 47.1
W = 12981.000
N = 12981
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 25
Class = Yes
AGE_OF_VEHICLE <= 5.50
Class Cases %
No 12328 54.2
Yes 10426 45.8
W = 22754.000
N = 22754
YEAR_OF_ACCIDENT <= 1998.50
Terminal
Node 23
Class = No
Class Cases %
No 4746 59.3
Yes 3254 40.7
W = 8000.000
N = 8000
YEAR_OF_ACCIDENT > 1998.50
Terminal
Node 24
Class = Yes
Class Cases %
No 767 55.9
Yes 604 44.1
W = 1371.000
N = 1371
MODEL_YEAR_OF_VEHICLE <= 1997.50
Node 28
Class = No
YEAR_OF_ACCIDENT <= 1998.50
Class Cases %
No 5513 58.8
Yes 3858 41.2
W = 9371.000
N = 9371
MODEL_YEAR_OF_VEHICLE > 1997.50
Terminal
Node 25
Class = Yes
Class Cases %
No 151 47.9
Yes 164 52.1
W = 315.000
N = 315
OCCUPANT_AGE_GROUPS$ = (16 to 20)
Node 27
Class = No
MODEL_YEAR_OF_VEHICLE <= 1997.50
Class Cases %
No 5664 58.5
Yes 4022 41.5
W = 9686.000
N = 9686
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 24
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(12 to 15,21 to 24,
25 to 34)
Class Cases %
No 17992 55.5
Yes 14448 44.5
W = 32440.000
N = 32440
YEAR_OF_ACCIDENT <= 2003.50
Node 23
Class = Yes
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 22834 53.5
Yes 19825 46.5
W = 42659.000
N = 42659
YEAR_OF_ACCIDENT > 2003.50
Terminal
Node 26
Class = Yes
Class Cases %
No 3453 42.1
Yes 4757 57.9
W = 8210.000
N = 8210
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 22
Class = Yes
YEAR_OF_ACCIDENT <= 2003.50
Class Cases %
No 26287 51.7
Yes 24582 48.3
W = 50869.000
N = 50869
CAR_COMPANY_HQ_REGION$ = (USA)
Node 21
Class = Yes
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 30086 48.9
Yes 31444 51.1
W = 61530.000
N = 61530
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 20
Class = Yes
CAR_COMPANY_HQ_REGION$ =
(Europe,Japan,Korea,
Other Imports)
Class Cases %
No 38645 45.4
Yes 46538 54.6
W = 85183.000
N = 85183
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 27
Class = Yes
Class Cases %
No 110 41.7
Yes 154 58.3
W = 264.000
N = 264
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 28
Class = No
Class Cases %
No 67 69.1
Yes 30 30.9
W = 97.000
N = 97
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 32
Class = Yes
TRAVEL_SPEED_GROUP$ = (16 to 35)
Class Cases %
No 177 49.0
Yes 184 51.0
W = 361.000
N = 361
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 29
Class = No
Class Cases %
No 689 64.2
Yes 385 35.8
W = 1074.000
N = 1074
YEAR_OF_ACCIDENT <= 2007.50
Node 31
Class = No
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 866 60.3
Yes 569 39.7
W = 1435.000
N = 1435
YEAR_OF_ACCIDENT > 2007.50
Terminal
Node 30
Class = Yes
Class Cases %
No 89 40.6
Yes 130 59.4
W = 219.000
N = 219
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Node 30
Class = No
YEAR_OF_ACCIDENT <= 2007.50
Class Cases %
No 955 57.7
Yes 699 42.3
W = 1654.000
N = 1654
CAR_COMPANY_HQ_REGION$ = (Other Impo...)
Terminal
Node 31
Class = No
Class Cases %
No 3202 73.1
Yes 1180 26.9
W = 4382.000
N = 4382
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 29
Class = No
CAR_COMPANY_HQ_REGION$ =
(Europe,Japan,Korea)
Class Cases %
No 4157 68.9
Yes 1879 31.1
W = 6036.000
N = 6036
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 19
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 42802 46.9
Yes 48417 53.1
W = 91219.000
N = 91219
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 32
Class = Yes
Class Cases %
No 93 34.2
Yes 179 65.8
W = 272.000
N = 272
MODEL_YEAR_OF_VEHICLE <= 1977.50
Terminal
Node 33
Class = No
Class Cases %
No 5613 66.4
Yes 2843 33.6
W = 8456.000
N = 8456
YEAR_OF_ACCIDENT <= 1985.50
Terminal
Node 34
Class = No
Class Cases %
No 2365 62.8
Yes 1400 37.2
W = 3765.000
N = 3765
OCCUPANT_AGE_GROUPS$ = (21 to 24,...)
Terminal
Node 35
Class = Yes
Class Cases %
No 1586 53.3
Yes 1391 46.7
W = 2977.000
N = 2977
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 36
Class = No
Class Cases %
No 514 64.3
Yes 286 35.8
W = 800.000
N = 800
YEAR_OF_ACCIDENT > 1985.50
Node 39
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(21 to 24,25 to 34,
35 to 44)
Class Cases %
No 2100 55.6
Yes 1677 44.4
W = 3777.000
N = 3777
MODEL_YEAR_OF_VEHICLE > 1977.50
Node 38
Class = No
YEAR_OF_ACCIDENT <= 1985.50
Class Cases %
No 4465 59.2
Yes 3077 40.8
W = 7542.000
N = 7542
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 37
Class = No
MODEL_YEAR_OF_VEHICLE <= 1977.50
Class Cases %
No 10078 63.0
Yes 5920 37.0
W = 15998.000
N = 15998
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 36
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 10171 62.5
Yes 6099 37.5
W = 16270.000
N = 16270
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 37
Class = No
Class Cases %
No 3680 72.7
Yes 1385 27.3
W = 5065.000
N = 5065
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 35
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 13851 64.9
Yes 7484 35.1
W = 21335.000
N = 21335
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 38
Class = No
Class Cases %
No 1328 84.6
Yes 241 15.4
W = 1569.000
N = 1569
YEAR_OF_ACCIDENT <= 1993.50
Node 34
Class = No
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 15179 66.3
Yes 7725 33.7
W = 22904.000
N = 22904
MODEL_YEAR_OF_VEHICLE <= 1976.50
Terminal
Node 39
Class = No
Class Cases %
No 80 61.5
Yes 50 38.5
W = 130.000
N = 130
MODEL_YEAR_OF_VEHICLE > 1976.50
Terminal
Node 40
Class = Yes
Class Cases %
No 947 37.5
Yes 1575 62.5
W = 2522.000
N = 2522
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 42
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 1976.50
Class Cases %
No 1027 38.7
Yes 1625 61.3
W = 2652.000
N = 2652
MODEL_YEAR_OF_VEHICLE <= 1979.50
Terminal
Node 41
Class = No
Class Cases %
No 357 64.8
Yes 194 35.2
W = 551.000
N = 551
YEAR_OF_ACCIDENT <= 1995.50
Terminal
Node 42
Class = No
Class Cases %
No 321 59.3
Yes 220 40.7
W = 541.000
N = 541
YEAR_OF_ACCIDENT > 1995.50
Terminal
Node 43
Class = Yes
Class Cases %
No 691 52.0
Yes 638 48.0
W = 1329.000
N = 1329
MODEL_YEAR_OF_VEHICLE > 1979.50
Node 46
Class = Yes
YEAR_OF_ACCIDENT <= 1995.50
Class Cases %
No 1012 54.1
Yes 858 45.9
W = 1870.000
N = 1870
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 45
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 1979.50
Class Cases %
No 1369 56.5
Yes 1052 43.5
W = 2421.000
N = 2421
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 44
Class = No
Class Cases %
No 257 66.8
Yes 128 33.2
W = 385.000
N = 385
YEAR_OF_ACCIDENT <= 2000.50
Node 44
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 1626 57.9
Yes 1180 42.1
W = 2806.000
N = 2806
YEAR_OF_ACCIDENT > 2000.50
Terminal
Node 45
Class = Yes
Class Cases %
No 1821 46.8
Yes 2067 53.2
W = 3888.000
N = 3888
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 43
Class = Yes
YEAR_OF_ACCIDENT <= 2000.50
Class Cases %
No 3447 51.5
Yes 3247 48.5
W = 6694.000
N = 6694
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 41
Class = Yes
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 4474 47.9
Yes 4872 52.1
W = 9346.000
N = 9346
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 46
Class = No
Class Cases %
No 438 73.4
Yes 159 26.6
W = 597.000
N = 597
YEAR_OF_ACCIDENT > 1993.50
Node 40
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 4912 49.4
Yes 5031 50.6
W = 9943.000
N = 9943
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 33
Class = No
YEAR_OF_ACCIDENT <= 1993.50
Class Cases %
No 20091 61.2
Yes 12756 38.8
W = 32847.000
N = 32847
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 18
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 62893 50.7
Yes 61173 49.3
W = 124066.000
N = 124066
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 17
Class = Yes
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 72832 48.5
Yes 77435 51.5
W = 150267.000
N = 150267
EXTRICATED_Y_N$ = (No)
Node 3
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(0 to 11,45 to 54,
55 to 64,65 to 74,
75+)
Class Cases %
No 91908 42.4
Yes 124834 57.6
W = 216742.000
N = 216742
SB_USED_Y_N$ = (No)
Node 2
Class = Yes
EXTRICATED_Y_N$ = (Yes)
Class Cases %
No 98774 38.6
Yes 157374 61.4
W = 256148.000
N = 256148
EXTRICATED_Y_N$ = (Yes)
Terminal
Node 47
Class = Yes
Class Cases %
No 13643 31.7
Yes 29455 68.3
W = 43098.000
N = 43098
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Terminal
Node 48
Class = Yes
Class Cases %
No 3290 36.3
Yes 5770 63.7
W = 9060.000
N = 9060
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 49
Class = Yes
Class Cases %
No 4541 46.0
Yes 5334 54.0
W = 9875.000
N = 9875
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 50
Class = No
Class Cases %
No 474 63.9
Yes 268 36.1
W = 742.000
N = 742
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 51
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 5015 47.2
Yes 5602 52.8
W = 10617.000
N = 10617
OCCUPANT_AGE_GROUPS$ = (75+)
Node 50
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 8305 42.2
Yes 11372 57.8
W = 19677.000
N = 19677
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Terminal
Node 51
Class = Yes
Class Cases %
No 612 38.3
Yes 987 61.7
W = 1599.000
N = 1599
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Terminal
Node 52
Class = Yes
Class Cases %
No 857 50.8
Yes 831 49.2
W = 1688.000
N = 1688
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 53
Class = No
Class Cases %
No 108 64.3
Yes 60 35.7
W = 168.000
N = 168
SEX$ = (Female)
Node 57
Class = Yes
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 965 52.0
Yes 891 48.0
W = 1856.000
N = 1856
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 54
Class = Yes
Class Cases %
No 127 53.4
Yes 111 46.6
W = 238.000
N = 238
AGE_OF_VEHICLE <= 6.50
Terminal
Node 55
Class = No
Class Cases %
No 151 61.9
Yes 93 38.1
W = 244.000
N = 244
AGE_OF_VEHICLE > 6.50
Terminal
Node 56
Class = Yes
Class Cases %
No 104 48.6
Yes 110 51.4
W = 214.000
N = 214
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Node 60
Class = Yes
AGE_OF_VEHICLE <= 6.50
Class Cases %
No 255 55.7
Yes 203 44.3
W = 458.000
N = 458
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Terminal
Node 57
Class = No
Class Cases %
No 1818 64.9
Yes 985 35.1
W = 2803.000
N = 2803
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 59
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Other Imports)
Class Cases %
No 2073 63.6
Yes 1188 36.4
W = 3261.000
N = 3261
SEX$ = (*,Male)
Node 58
Class = No
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 2200 62.9
Yes 1299 37.1
W = 3499.000
N = 3499
YEAR_OF_ACCIDENT <= 2003.50
Node 56
Class = No
SEX$ = (Female)
Class Cases %
No 3165 59.1
Yes 2190 40.9
W = 5355.000
N = 5355
YEAR_OF_ACCIDENT > 2003.50
Terminal
Node 58
Class = Yes
Class Cases %
No 825 47.1
Yes 925 52.9
W = 1750.000
N = 1750
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 55
Class = Yes
YEAR_OF_ACCIDENT <= 2003.50
Class Cases %
No 3990 56.2
Yes 3115 43.8
W = 7105.000
N = 7105
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 54
Class = Yes
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 4602 52.9
Yes 4102 47.1
W = 8704.000
N = 8704
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 59
Class = No
Class Cases %
No 407 76.5
Yes 125 23.5
W = 532.000
N = 532
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 53
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 5009 54.2
Yes 4227 45.8
W = 9236.000
N = 9236
TRAVEL_SPEED_GROUP$ = (> 75)
Terminal
Node 60
Class = Yes
Class Cases %
No 23 41.8
Yes 32 58.2
W = 55.000
N = 55
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 61
Class = No
Class Cases %
No 4383 68.0
Yes 2060 32.0
W = 6443.000
N = 6443
AGE_OF_VEHICLE <= 8.50
Node 64
Class = No
TRAVEL_SPEED_GROUP$ = (> 75)
Class Cases %
No 4406 67.8
Yes 2092 32.2
W = 6498.000
N = 6498
YEAR_OF_ACCIDENT <= 1992.50
Terminal
Node 62
Class = No
Class Cases %
No 166 72.2
Yes 64 27.8
W = 230.000
N = 230
MODEL_YEAR_OF_VEHICLE <= 2001.50
Terminal
Node 63
Class = Yes
Class Cases %
No 313 47.9
Yes 340 52.1
W = 653.000
N = 653
MODEL_YEAR_OF_VEHICLE > 2001.50
Terminal
Node 64
Class = No
Class Cases %
No 272 58.6
Yes 192 41.4
W = 464.000
N = 464
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Node 68
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 2001.50
Class Cases %
No 585 52.4
Yes 532 47.6
W = 1117.000
N = 1117
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 65
Class = No
Class Cases %
No 73 62.9
Yes 43 37.1
W = 116.000
N = 116
SEX$ = (*,Female)
Node 67
Class = Yes
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 658 53.4
Yes 575 46.6
W = 1233.000
N = 1233
AGE_OF_VEHICLE <= 14.50
Terminal
Node 66
Class = No
Class Cases %
No 1068 61.2
Yes 677 38.8
W = 1745.000
N = 1745
AGE_OF_VEHICLE > 14.50
Terminal
Node 67
Class = Yes
Class Cases %
No 336 54.8
Yes 277 45.2
W = 613.000
N = 613
SEX$ = (Male)
Node 69
Class = No
AGE_OF_VEHICLE <= 14.50
Class Cases %
No 1404 59.5
Yes 954 40.5
W = 2358.000
N = 2358
YEAR_OF_ACCIDENT > 1992.50
Node 66
Class = No
SEX$ = (*,Female)
Class Cases %
No 2062 57.4
Yes 1529 42.6
W = 3591.000
N = 3591
AGE_OF_VEHICLE > 8.50
Node 65
Class = No
YEAR_OF_ACCIDENT <= 1992.50
Class Cases %
No 2228 58.3
Yes 1593 41.7
W = 3821.000
N = 3821
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 63
Class = No
AGE_OF_VEHICLE <= 8.50
Class Cases %
No 6634 64.3
Yes 3685 35.7
W = 10319.000
N = 10319
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 68
Class = No
Class Cases %
No 1237 72.7
Yes 465 27.3
W = 1702.000
N = 1702
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 62
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 7871 65.5
Yes 4150 34.5
W = 12021.000
N = 12021
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 69
Class = No
Class Cases %
No 619 84.8
Yes 111 15.2
W = 730.000
N = 730
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 61
Class = No
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 8490 66.6
Yes 4261 33.4
W = 12751.000
N = 12751
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 52
Class = No
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 13499 61.4
Yes 8488 38.6
W = 21987.000
N = 21987
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 49
Class = Yes
OCCUPANT_AGE_GROUPS$ = (75+)
Class Cases %
No 21804 52.3
Yes 19860 47.7
W = 41664.000
N = 41664
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 70
Class = No
Class Cases %
No 9284 69.0
Yes 4172 31.0
W = 13456.000
N = 13456
YEAR_OF_ACCIDENT <= 1980.50
Terminal
Node 71
Class = No
Class Cases %
No 43 70.5
Yes 18 29.5
W = 61.000
N = 61
AGE_OF_VEHICLE <= 5.50
Terminal
Node 72
Class = No
Class Cases %
No 368 58.0
Yes 266 42.0
W = 634.000
N = 634
AGE_OF_VEHICLE > 5.50
Terminal
Node 73
Class = Yes
Class Cases %
No 38 38.4
Yes 61 61.6
W = 99.000
N = 99
AGE_OF_VEHICLE <= 6.50
Node 78
Class = Yes
AGE_OF_VEHICLE <= 5.50
Class Cases %
No 406 55.4
Yes 327 44.6
W = 733.000
N = 733
AGE_OF_VEHICLE > 6.50
Terminal
Node 74
Class = No
Class Cases %
No 162 63.0
Yes 95 37.0
W = 257.000
N = 257
YEAR_OF_ACCIDENT > 1980.50
Node 77
Class = No
AGE_OF_VEHICLE <= 6.50
Class Cases %
No 568 57.4
Yes 422 42.6
W = 990.000
N = 990
YEAR_OF_ACCIDENT <= 1990.50
Node 76
Class = No
YEAR_OF_ACCIDENT <= 1980.50
Class Cases %
No 611 58.1
Yes 440 41.9
W = 1051.000
N = 1051
YEAR_OF_ACCIDENT > 1990.50
Terminal
Node 75
Class = Yes
Class Cases %
No 1386 46.7
Yes 1584 53.3
W = 2970.000
N = 2970
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 75
Class = Yes
YEAR_OF_ACCIDENT <= 1990.50
Class Cases %
No 1997 49.7
Yes 2024 50.3
W = 4021.000
N = 4021
MODEL_YEAR_OF_VEHICLE <= 1988.50
Terminal
Node 76
Class = No
Class Cases %
No 954 63.5
Yes 548 36.5
W = 1502.000
N = 1502
MODEL_YEAR_OF_VEHICLE <= 1994.50
Terminal
Node 77
Class = Yes
Class Cases %
No 485 53.7
Yes 419 46.3
W = 904.000
N = 904
MODEL_YEAR_OF_VEHICLE > 1994.50
Terminal
Node 78
Class = No
Class Cases %
No 331 64.6
Yes 181 35.4
W = 512.000
N = 512
MODEL_YEAR_OF_VEHICLE > 1988.50
Node 82
Class = No
MODEL_YEAR_OF_VEHICLE <= 1994.50
Class Cases %
No 816 57.6
Yes 600 42.4
W = 1416.000
N = 1416
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 81
Class = No
MODEL_YEAR_OF_VEHICLE <= 1988.50
Class Cases %
No 1770 60.7
Yes 1148 39.3
W = 2918.000
N = 2918
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 79
Class = No
Class Cases %
No 7443 67.4
Yes 3598 32.6
W = 11041.000
N = 11041
AGE_OF_VEHICLE <= 13.50
Node 80
Class = No
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 9213 66.0
Yes 4746 34.0
W = 13959.000
N = 13959
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 80
Class = Yes
Class Cases %
No 584 52.1
Yes 536 47.9
W = 1120.000
N = 1120
OCCUPANT_AGE_GROUPS$ = (16 to 20)
Terminal
Node 81
Class = No
Class Cases %
No 246 62.8
Yes 146 37.2
W = 392.000
N = 392
AGE_OF_VEHICLE > 13.50
Node 83
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(12 to 15,21 to 24,
25 to 34,35 to 44)
Class Cases %
No 830 54.9
Yes 682 45.1
W = 1512.000
N = 1512
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 79
Class = No
AGE_OF_VEHICLE <= 13.50
Class Cases %
No 10043 64.9
Yes 5428 35.1
W = 15471.000
N = 15471
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Node 74
Class = No
OCCUPANT_AGE_GROUPS$ =
(45 to 54,55 to 64)
Class Cases %
No 12040 61.8
Yes 7452 38.2
W = 19492.000
N = 19492
YEAR_OF_ACCIDENT <= 2004.50
Terminal
Node 82
Class = No
Class Cases %
No 13382 75.5
Yes 4347 24.5
W = 17729.000
N = 17729
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 83
Class = Yes
Class Cases %
No 139 37.0
Yes 237 63.0
W = 376.000
N = 376
AGE_OF_VEHICLE <= 16.50
Terminal
Node 84
Class = No
Class Cases %
No 148 69.5
Yes 65 30.5
W = 213.000
N = 213
OCCUPANT_AGE_GROUPS$ = (55 to 64)
Terminal
Node 85
Class = Yes
Class Cases %
No 180 52.8
Yes 161 47.2
W = 341.000
N = 341
OCCUPANT_AGE_GROUPS$ = (45 to 54)
Terminal
Node 86
Class = No
Class Cases %
No 298 65.5
Yes 157 34.5
W = 455.000
N = 455
AGE_OF_VEHICLE > 16.50
Node 89
Class = No
OCCUPANT_AGE_GROUPS$ = (55 to 64)
Class Cases %
No 478 60.1
Yes 318 39.9
W = 796.000
N = 796
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 88
Class = No
AGE_OF_VEHICLE <= 16.50
Class Cases %
No 626 62.0
Yes 383 38.0
W = 1009.000
N = 1009
MODEL_YEAR_OF_VEHICLE <= 1992.50
Node 87
Class = Yes
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 765 55.2
Yes 620 44.8
W = 1385.000
N = 1385
MODEL_YEAR_OF_VEHICLE > 1992.50
Terminal
Node 87
Class = No
Class Cases %
No 3023 66.9
Yes 1498 33.1
W = 4521.000
N = 4521
YEAR_OF_ACCIDENT > 2004.50
Node 86
Class = No
MODEL_YEAR_OF_VEHICLE <= 1992.50
Class Cases %
No 3788 64.1
Yes 2118 35.9
W = 5906.000
N = 5906
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 85
Class = No
YEAR_OF_ACCIDENT <= 2004.50
Class Cases %
No 17170 72.6
Yes 6465 27.4
W = 23635.000
N = 23635
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 88
Class = No
Class Cases %
No 54887 81.6
Yes 12350 18.4
W = 67237.000
N = 67237
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 84
Class = No
OCCUPANT_AGE_GROUPS$ =
(45 to 54,55 to 64)
Class Cases %
No 72057 79.3
Yes 18815 20.7
W = 90872.000
N = 90872
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 73
Class = No
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 84097 76.2
Yes 26267 23.8
W = 110364.000
N = 110364
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 72
Class = No
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 93381 75.4
Yes 30439 24.6
W = 123820.000
N = 123820
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Terminal
Node 89
Class = No
Class Cases %
No 86853 82.6
Yes 18285 17.4
W = 105138.000
N = 105138
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Node 71
Class = No
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 180234 78.7
Yes 48724 21.3
W = 228958.000
N = 228958
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 90
Class = No
Class Cases %
No 23988 92.8
Yes 1866 7.2
W = 25854.000
N = 25854
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 70
Class = No
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 204222 80.1
Yes 50590 19.9
W = 254812.000
N = 254812
EXTRICATED_Y_N$ = (No)
Node 48
Class = No
OCCUPANT_AGE_GROUPS$ =
(0 to 11,65 to 74,
75+)
Class Cases %
No 226026 76.2
Yes 70450 23.8
W = 296476.000
N = 296476
SB_USED_Y_N$ = (Yes)
Node 47
Class = No
EXTRICATED_Y_N$ = (Yes)
Class Cases %
No 239669 70.6
Yes 99905 29.4
W = 339574.000
N = 339574
Node 1
Class = Yes
SB_USED_Y_N$ = (No)
Class Cases %
No 338443 56.8
Yes 257279 43.2
W = 595722.000
N = 595722
SB wornNo SB Worn
Lower
Fatalities
Higher
Fatalities
CART TREE TOPOGRAPHY: DETAILED NODE
DATA BOX VIEW
43
EXTRICATED_Y_N$ = (Yes)
Terminal
Node 1
Class = Yes
Class Cases %
No 6866 17.4
Yes 32540 82.6
W = 39406.000
N = 39406
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 2
Class = Yes
Class Cases %
No 3922 32.6
Yes 8113 67.4
W = 12035.000
N = 12035
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Terminal
Node 3
Class = Yes
Class Cases %
No 33 28.2
Yes 84 71.8
W = 117.000
N = 117
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Terminal
Node 4
Class = No
Class Cases %
No 189 63.2
Yes 110 36.8
W = 299.000
N = 299
TRAVEL_SPEED_GROUP$ = (0 to 15)
Node 7
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(0 to 11,75+)
Class Cases %
No 222 53.4
Yes 194 46.6
W = 416.000
N = 416
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 6
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 4144 33.3
Yes 8307 66.7
W = 12451.000
N = 12451
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 5
Class = Yes
Class Cases %
No 102 28.3
Yes 258 71.7
W = 360.000
N = 360
OCCUPANT_AGE_GROUPS$ = (65 to 74,...)
Terminal
Node 6
Class = Yes
Class Cases %
No 1118 39.8
Yes 1692 60.2
W = 2810.000
N = 2810
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Terminal
Node 7
Class = Yes
Class Cases %
No 1685 48.6
Yes 1779 51.4
W = 3464.000
N = 3464
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 8
Class = Yes
Class Cases %
No 1 6.7
Yes 14 93.3
W = 15.000
N = 15
YEAR_OF_ACCIDENT <= 1984.50
Terminal
Node 9
Class = No
Class Cases %
No 866 60.2
Yes 573 39.8
W = 1439.000
N = 1439
YEAR_OF_ACCIDENT > 1984.50
Terminal
Node 10
Class = Yes
Class Cases %
No 309 52.8
Yes 276 47.2
W = 585.000
N = 585
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 13
Class = No
YEAR_OF_ACCIDENT <= 1984.50
Class Cases %
No 1175 58.1
Yes 849 41.9
W = 2024.000
N = 2024
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Node 12
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 1176 57.7
Yes 863 42.3
W = 2039.000
N = 2039
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 11
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 2861 52.0
Yes 2642 48.0
W = 5503.000
N = 5503
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 10
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(65 to 74,75+)
Class Cases %
No 3979 47.9
Yes 4334 52.1
W = 8313.000
N = 8313
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Terminal
Node 11
Class = Yes
Class Cases %
No 73 43.2
Yes 96 56.8
W = 169.000
N = 169
YEAR_OF_ACCIDENT <= 1987.50
Terminal
Node 12
Class = No
Class Cases %
No 452 72.2
Yes 174 27.8
W = 626.000
N = 626
OCCUPANT_AGE_GROUPS$ = (55 to 64,...)
Terminal
Node 13
Class = Yes
Class Cases %
No 32 38.1
Yes 52 61.9
W = 84.000
N = 84
OCCUPANT_AGE_GROUPS$ = (45 to 54)
Terminal
Node 14
Class = No
Class Cases %
No 43 71.7
Yes 17 28.3
W = 60.000
N = 60
YEAR_OF_ACCIDENT > 1987.50
Node 16
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(55 to 64,65 to 74)
Class Cases %
No 75 52.1
Yes 69 47.9
W = 144.000
N = 144
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 15
Class = No
YEAR_OF_ACCIDENT <= 1987.50
Class Cases %
No 527 68.4
Yes 243 31.6
W = 770.000
N = 770
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 14
Class = No
OCCUPANT_AGE_GROUPS$ =
(0 to 11,75+)
Class Cases %
No 600 63.9
Yes 339 36.1
W = 939.000
N = 939
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 9
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 4579 49.5
Yes 4673 50.5
W = 9252.000
N = 9252
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 8
Class = Yes
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 4681 48.7
Yes 4931 51.3
W = 9612.000
N = 9612
YEAR_OF_ACCIDENT <= 1989.50
Node 5
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 8825 40.0
Yes 13238 60.0
W = 22063.000
N = 22063
YEAR_OF_ACCIDENT > 1989.50
Terminal
Node 15
Class = Yes
Class Cases %
No 10251 23.1
Yes 34161 76.9
W = 44412.000
N = 44412
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 4
Class = Yes
YEAR_OF_ACCIDENT <= 1989.50
Class Cases %
No 19076 28.7
Yes 47399 71.3
W = 66475.000
N = 66475
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 16
Class = Yes
Class Cases %
No 9939 37.9
Yes 16262 62.1
W = 26201.000
N = 26201
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Terminal
Node 17
Class = Yes
Class Cases %
No 8559 36.2
Yes 15094 63.8
W = 23653.000
N = 23653
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Terminal
Node 18
Class = Yes
Class Cases %
No 3799 35.6
Yes 6862 64.4
W = 10661.000
N = 10661
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Terminal
Node 19
Class = Yes
Class Cases %
No 4842 47.4
Yes 5377 52.6
W = 10219.000
N = 10219
YEAR_OF_ACCIDENT <= 1985.50
Terminal
Node 20
Class = Yes
Class Cases %
No 1561 52.6
Yes 1409 47.4
W = 2970.000
N = 2970
YEAR_OF_ACCIDENT > 1985.50
Terminal
Node 21
Class = No
Class Cases %
No 3905 57.4
Yes 2898 42.6
W = 6803.000
N = 6803
AGE_OF_VEHICLE <= 5.50
Node 26
Class = Yes
YEAR_OF_ACCIDENT <= 1985.50
Class Cases %
No 5466 55.9
Yes 4307 44.1
W = 9773.000
N = 9773
AGE_OF_VEHICLE > 5.50
Terminal
Node 22
Class = Yes
Class Cases %
No 6862 52.9
Yes 6119 47.1
W = 12981.000
N = 12981
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 25
Class = Yes
AGE_OF_VEHICLE <= 5.50
Class Cases %
No 12328 54.2
Yes 10426 45.8
W = 22754.000
N = 22754
YEAR_OF_ACCIDENT <= 1998.50
Terminal
Node 23
Class = No
Class Cases %
No 4746 59.3
Yes 3254 40.7
W = 8000.000
N = 8000
YEAR_OF_ACCIDENT > 1998.50
Terminal
Node 24
Class = Yes
Class Cases %
No 767 55.9
Yes 604 44.1
W = 1371.000
N = 1371
MODEL_YEAR_OF_VEHICLE <= 1997.50
Node 28
Class = No
YEAR_OF_ACCIDENT <= 1998.50
Class Cases %
No 5513 58.8
Yes 3858 41.2
W = 9371.000
N = 9371
MODEL_YEAR_OF_VEHICLE > 1997.50
Terminal
Node 25
Class = Yes
Class Cases %
No 151 47.9
Yes 164 52.1
W = 315.000
N = 315
OCCUPANT_AGE_GROUPS$ = (16 to 20)
Node 27
Class = No
MODEL_YEAR_OF_VEHICLE <= 1997.50
Class Cases %
No 5664 58.5
Yes 4022 41.5
W = 9686.000
N = 9686
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 24
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(12 to 15,21 to 24,
25 to 34)
Class Cases %
No 17992 55.5
Yes 14448 44.5
W = 32440.000
N = 32440
YEAR_OF_ACCIDENT <= 2003.50
Node 23
Class = Yes
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 22834 53.5
Yes 19825 46.5
W = 42659.000
N = 42659
YEAR_OF_ACCIDENT > 2003.50
Terminal
Node 26
Class = Yes
Class Cases %
No 3453 42.1
Yes 4757 57.9
W = 8210.000
N = 8210
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 22
Class = Yes
YEAR_OF_ACCIDENT <= 2003.50
Class Cases %
No 26287 51.7
Yes 24582 48.3
W = 50869.000
N = 50869
CAR_COMPANY_HQ_REGION$ = (USA)
Node 21
Class = Yes
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 30086 48.9
Yes 31444 51.1
W = 61530.000
N = 61530
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 20
Class = Yes
CAR_COMPANY_HQ_REGION$ =
(Europe,Japan,Korea,
Other Imports)
Class Cases %
No 38645 45.4
Yes 46538 54.6
W = 85183.000
N = 85183
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 27
Class = Yes
Class Cases %
No 110 41.7
Yes 154 58.3
W = 264.000
N = 264
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 28
Class = No
Class Cases %
No 67 69.1
Yes 30 30.9
W = 97.000
N = 97
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 32
Class = Yes
TRAVEL_SPEED_GROUP$ = (16 to 35)
Class Cases %
No 177 49.0
Yes 184 51.0
W = 361.000
N = 361
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 29
Class = No
Class Cases %
No 689 64.2
Yes 385 35.8
W = 1074.000
N = 1074
YEAR_OF_ACCIDENT <= 2007.50
Node 31
Class = No
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 866 60.3
Yes 569 39.7
W = 1435.000
N = 1435
YEAR_OF_ACCIDENT > 2007.50
Terminal
Node 30
Class = Yes
Class Cases %
No 89 40.6
Yes 130 59.4
W = 219.000
N = 219
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Node 30
Class = No
YEAR_OF_ACCIDENT <= 2007.50
Class Cases %
No 955 57.7
Yes 699 42.3
W = 1654.000
N = 1654
CAR_COMPANY_HQ_REGION$ = (Other Impo...)
Terminal
Node 31
Class = No
Class Cases %
No 3202 73.1
Yes 1180 26.9
W = 4382.000
N = 4382
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 29
Class = No
CAR_COMPANY_HQ_REGION$ =
(Europe,Japan,Korea)
Class Cases %
No 4157 68.9
Yes 1879 31.1
W = 6036.000
N = 6036
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 19
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 42802 46.9
Yes 48417 53.1
W = 91219.000
N = 91219
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 32
Class = Yes
Class Cases %
No 93 34.2
Yes 179 65.8
W = 272.000
N = 272
MODEL_YEAR_OF_VEHICLE <= 1977.50
Terminal
Node 33
Class = No
Class Cases %
No 5613 66.4
Yes 2843 33.6
W = 8456.000
N = 8456
YEAR_OF_ACCIDENT <= 1985.50
Terminal
Node 34
Class = No
Class Cases %
No 2365 62.8
Yes 1400 37.2
W = 3765.000
N = 3765
OCCUPANT_AGE_GROUPS$ = (21 to 24,...)
Terminal
Node 35
Class = Yes
Class Cases %
No 1586 53.3
Yes 1391 46.7
W = 2977.000
N = 2977
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 36
Class = No
Class Cases %
No 514 64.3
Yes 286 35.8
W = 800.000
N = 800
YEAR_OF_ACCIDENT > 1985.50
Node 39
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(21 to 24,25 to 34,
35 to 44)
Class Cases %
No 2100 55.6
Yes 1677 44.4
W = 3777.000
N = 3777
MODEL_YEAR_OF_VEHICLE > 1977.50
Node 38
Class = No
YEAR_OF_ACCIDENT <= 1985.50
Class Cases %
No 4465 59.2
Yes 3077 40.8
W = 7542.000
N = 7542
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 37
Class = No
MODEL_YEAR_OF_VEHICLE <= 1977.50
Class Cases %
No 10078 63.0
Yes 5920 37.0
W = 15998.000
N = 15998
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 36
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 10171 62.5
Yes 6099 37.5
W = 16270.000
N = 16270
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 37
Class = No
Class Cases %
No 3680 72.7
Yes 1385 27.3
W = 5065.000
N = 5065
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 35
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 13851 64.9
Yes 7484 35.1
W = 21335.000
N = 21335
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 38
Class = No
Class Cases %
No 1328 84.6
Yes 241 15.4
W = 1569.000
N = 1569
YEAR_OF_ACCIDENT <= 1993.50
Node 34
Class = No
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 15179 66.3
Yes 7725 33.7
W = 22904.000
N = 22904
MODEL_YEAR_OF_VEHICLE <= 1976.50
Terminal
Node 39
Class = No
Class Cases %
No 80 61.5
Yes 50 38.5
W = 130.000
N = 130
MODEL_YEAR_OF_VEHICLE > 1976.50
Terminal
Node 40
Class = Yes
Class Cases %
No 947 37.5
Yes 1575 62.5
W = 2522.000
N = 2522
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 42
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 1976.50
Class Cases %
No 1027 38.7
Yes 1625 61.3
W = 2652.000
N = 2652
MODEL_YEAR_OF_VEHICLE <= 1979.50
Terminal
Node 41
Class = No
Class Cases %
No 357 64.8
Yes 194 35.2
W = 551.000
N = 551
YEAR_OF_ACCIDENT <= 1995.50
Terminal
Node 42
Class = No
Class Cases %
No 321 59.3
Yes 220 40.7
W = 541.000
N = 541
YEAR_OF_ACCIDENT > 1995.50
Terminal
Node 43
Class = Yes
Class Cases %
No 691 52.0
Yes 638 48.0
W = 1329.000
N = 1329
MODEL_YEAR_OF_VEHICLE > 1979.50
Node 46
Class = Yes
YEAR_OF_ACCIDENT <= 1995.50
Class Cases %
No 1012 54.1
Yes 858 45.9
W = 1870.000
N = 1870
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 45
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 1979.50
Class Cases %
No 1369 56.5
Yes 1052 43.5
W = 2421.000
N = 2421
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 44
Class = No
Class Cases %
No 257 66.8
Yes 128 33.2
W = 385.000
N = 385
YEAR_OF_ACCIDENT <= 2000.50
Node 44
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 1626 57.9
Yes 1180 42.1
W = 2806.000
N = 2806
YEAR_OF_ACCIDENT > 2000.50
Terminal
Node 45
Class = Yes
Class Cases %
No 1821 46.8
Yes 2067 53.2
W = 3888.000
N = 3888
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 43
Class = Yes
YEAR_OF_ACCIDENT <= 2000.50
Class Cases %
No 3447 51.5
Yes 3247 48.5
W = 6694.000
N = 6694
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 41
Class = Yes
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 4474 47.9
Yes 4872 52.1
W = 9346.000
N = 9346
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 46
Class = No
Class Cases %
No 438 73.4
Yes 159 26.6
W = 597.000
N = 597
YEAR_OF_ACCIDENT > 1993.50
Node 40
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 4912 49.4
Yes 5031 50.6
W = 9943.000
N = 9943
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 33
Class = No
YEAR_OF_ACCIDENT <= 1993.50
Class Cases %
No 20091 61.2
Yes 12756 38.8
W = 32847.000
N = 32847
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 18
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 62893 50.7
Yes 61173 49.3
W = 124066.000
N = 124066
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 17
Class = Yes
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 72832 48.5
Yes 77435 51.5
W = 150267.000
N = 150267
EXTRICATED_Y_N$ = (No)
Node 3
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(0 to 11,45 to 54,
55 to 64,65 to 74,
75+)
Class Cases %
No 91908 42.4
Yes 124834 57.6
W = 216742.000
N = 216742
SB_USED_Y_N$ = (No)
Node 2
Class = Yes
EXTRICATED_Y_N$ = (Yes)
Class Cases %
No 98774 38.6
Yes 157374 61.4
W = 256148.000
N = 256148
EXTRICATED_Y_N$ = (Yes)
Terminal
Node 47
Class = Yes
Class Cases %
No 13643 31.7
Yes 29455 68.3
W = 43098.000
N = 43098
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Terminal
Node 48
Class = Yes
Class Cases %
No 3290 36.3
Yes 5770 63.7
W = 9060.000
N = 9060
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 49
Class = Yes
Class Cases %
No 4541 46.0
Yes 5334 54.0
W = 9875.000
N = 9875
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 50
Class = No
Class Cases %
No 474 63.9
Yes 268 36.1
W = 742.000
N = 742
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 51
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 5015 47.2
Yes 5602 52.8
W = 10617.000
N = 10617
OCCUPANT_AGE_GROUPS$ = (75+)
Node 50
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 8305 42.2
Yes 11372 57.8
W = 19677.000
N = 19677
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Terminal
Node 51
Class = Yes
Class Cases %
No 612 38.3
Yes 987 61.7
W = 1599.000
N = 1599
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Terminal
Node 52
Class = Yes
Class Cases %
No 857 50.8
Yes 831 49.2
W = 1688.000
N = 1688
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 53
Class = No
Class Cases %
No 108 64.3
Yes 60 35.7
W = 168.000
N = 168
SEX$ = (Female)
Node 57
Class = Yes
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 965 52.0
Yes 891 48.0
W = 1856.000
N = 1856
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 54
Class = Yes
Class Cases %
No 127 53.4
Yes 111 46.6
W = 238.000
N = 238
AGE_OF_VEHICLE <= 6.50
Terminal
Node 55
Class = No
Class Cases %
No 151 61.9
Yes 93 38.1
W = 244.000
N = 244
AGE_OF_VEHICLE > 6.50
Terminal
Node 56
Class = Yes
Class Cases %
No 104 48.6
Yes 110 51.4
W = 214.000
N = 214
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Node 60
Class = Yes
AGE_OF_VEHICLE <= 6.50
Class Cases %
No 255 55.7
Yes 203 44.3
W = 458.000
N = 458
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Terminal
Node 57
Class = No
Class Cases %
No 1818 64.9
Yes 985 35.1
W = 2803.000
N = 2803
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 59
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Other Imports)
Class Cases %
No 2073 63.6
Yes 1188 36.4
W = 3261.000
N = 3261
SEX$ = (*,Male)
Node 58
Class = No
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 2200 62.9
Yes 1299 37.1
W = 3499.000
N = 3499
YEAR_OF_ACCIDENT <= 2003.50
Node 56
Class = No
SEX$ = (Female)
Class Cases %
No 3165 59.1
Yes 2190 40.9
W = 5355.000
N = 5355
YEAR_OF_ACCIDENT > 2003.50
Terminal
Node 58
Class = Yes
Class Cases %
No 825 47.1
Yes 925 52.9
W = 1750.000
N = 1750
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 55
Class = Yes
YEAR_OF_ACCIDENT <= 2003.50
Class Cases %
No 3990 56.2
Yes 3115 43.8
W = 7105.000
N = 7105
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 54
Class = Yes
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 4602 52.9
Yes 4102 47.1
W = 8704.000
N = 8704
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 59
Class = No
Class Cases %
No 407 76.5
Yes 125 23.5
W = 532.000
N = 532
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 53
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 5009 54.2
Yes 4227 45.8
W = 9236.000
N = 9236
TRAVEL_SPEED_GROUP$ = (> 75)
Terminal
Node 60
Class = Yes
Class Cases %
No 23 41.8
Yes 32 58.2
W = 55.000
N = 55
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 61
Class = No
Class Cases %
No 4383 68.0
Yes 2060 32.0
W = 6443.000
N = 6443
AGE_OF_VEHICLE <= 8.50
Node 64
Class = No
TRAVEL_SPEED_GROUP$ = (> 75)
Class Cases %
No 4406 67.8
Yes 2092 32.2
W = 6498.000
N = 6498
YEAR_OF_ACCIDENT <= 1992.50
Terminal
Node 62
Class = No
Class Cases %
No 166 72.2
Yes 64 27.8
W = 230.000
N = 230
MODEL_YEAR_OF_VEHICLE <= 2001.50
Terminal
Node 63
Class = Yes
Class Cases %
No 313 47.9
Yes 340 52.1
W = 653.000
N = 653
MODEL_YEAR_OF_VEHICLE > 2001.50
Terminal
Node 64
Class = No
Class Cases %
No 272 58.6
Yes 192 41.4
W = 464.000
N = 464
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Node 68
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 2001.50
Class Cases %
No 585 52.4
Yes 532 47.6
W = 1117.000
N = 1117
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 65
Class = No
Class Cases %
No 73 62.9
Yes 43 37.1
W = 116.000
N = 116
SEX$ = (*,Female)
Node 67
Class = Yes
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 658 53.4
Yes 575 46.6
W = 1233.000
N = 1233
AGE_OF_VEHICLE <= 14.50
Terminal
Node 66
Class = No
Class Cases %
No 1068 61.2
Yes 677 38.8
W = 1745.000
N = 1745
AGE_OF_VEHICLE > 14.50
Terminal
Node 67
Class = Yes
Class Cases %
No 336 54.8
Yes 277 45.2
W = 613.000
N = 613
SEX$ = (Male)
Node 69
Class = No
AGE_OF_VEHICLE <= 14.50
Class Cases %
No 1404 59.5
Yes 954 40.5
W = 2358.000
N = 2358
YEAR_OF_ACCIDENT > 1992.50
Node 66
Class = No
SEX$ = (*,Female)
Class Cases %
No 2062 57.4
Yes 1529 42.6
W = 3591.000
N = 3591
AGE_OF_VEHICLE > 8.50
Node 65
Class = No
YEAR_OF_ACCIDENT <= 1992.50
Class Cases %
No 2228 58.3
Yes 1593 41.7
W = 3821.000
N = 3821
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 63
Class = No
AGE_OF_VEHICLE <= 8.50
Class Cases %
No 6634 64.3
Yes 3685 35.7
W = 10319.000
N = 10319
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 68
Class = No
Class Cases %
No 1237 72.7
Yes 465 27.3
W = 1702.000
N = 1702
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 62
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 7871 65.5
Yes 4150 34.5
W = 12021.000
N = 12021
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 69
Class = No
Class Cases %
No 619 84.8
Yes 111 15.2
W = 730.000
N = 730
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 61
Class = No
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 8490 66.6
Yes 4261 33.4
W = 12751.000
N = 12751
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 52
Class = No
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 13499 61.4
Yes 8488 38.6
W = 21987.000
N = 21987
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 49
Class = Yes
OCCUPANT_AGE_GROUPS$ = (75+)
Class Cases %
No 21804 52.3
Yes 19860 47.7
W = 41664.000
N = 41664
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 70
Class = No
Class Cases %
No 9284 69.0
Yes 4172 31.0
W = 13456.000
N = 13456
YEAR_OF_ACCIDENT <= 1980.50
Terminal
Node 71
Class = No
Class Cases %
No 43 70.5
Yes 18 29.5
W = 61.000
N = 61
AGE_OF_VEHICLE <= 5.50
Terminal
Node 72
Class = No
Class Cases %
No 368 58.0
Yes 266 42.0
W = 634.000
N = 634
AGE_OF_VEHICLE > 5.50
Terminal
Node 73
Class = Yes
Class Cases %
No 38 38.4
Yes 61 61.6
W = 99.000
N = 99
AGE_OF_VEHICLE <= 6.50
Node 78
Class = Yes
AGE_OF_VEHICLE <= 5.50
Class Cases %
No 406 55.4
Yes 327 44.6
W = 733.000
N = 733
AGE_OF_VEHICLE > 6.50
Terminal
Node 74
Class = No
Class Cases %
No 162 63.0
Yes 95 37.0
W = 257.000
N = 257
YEAR_OF_ACCIDENT > 1980.50
Node 77
Class = No
AGE_OF_VEHICLE <= 6.50
Class Cases %
No 568 57.4
Yes 422 42.6
W = 990.000
N = 990
YEAR_OF_ACCIDENT <= 1990.50
Node 76
Class = No
YEAR_OF_ACCIDENT <= 1980.50
Class Cases %
No 611 58.1
Yes 440 41.9
W = 1051.000
N = 1051
YEAR_OF_ACCIDENT > 1990.50
Terminal
Node 75
Class = Yes
Class Cases %
No 1386 46.7
Yes 1584 53.3
W = 2970.000
N = 2970
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 75
Class = Yes
YEAR_OF_ACCIDENT <= 1990.50
Class Cases %
No 1997 49.7
Yes 2024 50.3
W = 4021.000
N = 4021
MODEL_YEAR_OF_VEHICLE <= 1988.50
Terminal
Node 76
Class = No
Class Cases %
No 954 63.5
Yes 548 36.5
W = 1502.000
N = 1502
MODEL_YEAR_OF_VEHICLE <= 1994.50
Terminal
Node 77
Class = Yes
Class Cases %
No 485 53.7
Yes 419 46.3
W = 904.000
N = 904
MODEL_YEAR_OF_VEHICLE > 1994.50
Terminal
Node 78
Class = No
Class Cases %
No 331 64.6
Yes 181 35.4
W = 512.000
N = 512
MODEL_YEAR_OF_VEHICLE > 1988.50
Node 82
Class = No
MODEL_YEAR_OF_VEHICLE <= 1994.50
Class Cases %
No 816 57.6
Yes 600 42.4
W = 1416.000
N = 1416
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 81
Class = No
MODEL_YEAR_OF_VEHICLE <= 1988.50
Class Cases %
No 1770 60.7
Yes 1148 39.3
W = 2918.000
N = 2918
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 79
Class = No
Class Cases %
No 7443 67.4
Yes 3598 32.6
W = 11041.000
N = 11041
AGE_OF_VEHICLE <= 13.50
Node 80
Class = No
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 9213 66.0
Yes 4746 34.0
W = 13959.000
N = 13959
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 80
Class = Yes
Class Cases %
No 584 52.1
Yes 536 47.9
W = 1120.000
N = 1120
OCCUPANT_AGE_GROUPS$ = (16 to 20)
Terminal
Node 81
Class = No
Class Cases %
No 246 62.8
Yes 146 37.2
W = 392.000
N = 392
AGE_OF_VEHICLE > 13.50
Node 83
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(12 to 15,21 to 24,
25 to 34,35 to 44)
Class Cases %
No 830 54.9
Yes 682 45.1
W = 1512.000
N = 1512
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 79
Class = No
AGE_OF_VEHICLE <= 13.50
Class Cases %
No 10043 64.9
Yes 5428 35.1
W = 15471.000
N = 15471
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Node 74
Class = No
OCCUPANT_AGE_GROUPS$ =
(45 to 54,55 to 64)
Class Cases %
No 12040 61.8
Yes 7452 38.2
W = 19492.000
N = 19492
YEAR_OF_ACCIDENT <= 2004.50
Terminal
Node 82
Class = No
Class Cases %
No 13382 75.5
Yes 4347 24.5
W = 17729.000
N = 17729
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 83
Class = Yes
Class Cases %
No 139 37.0
Yes 237 63.0
W = 376.000
N = 376
AGE_OF_VEHICLE <= 16.50
Terminal
Node 84
Class = No
Class Cases %
No 148 69.5
Yes 65 30.5
W = 213.000
N = 213
OCCUPANT_AGE_GROUPS$ = (55 to 64)
Terminal
Node 85
Class = Yes
Class Cases %
No 180 52.8
Yes 161 47.2
W = 341.000
N = 341
OCCUPANT_AGE_GROUPS$ = (45 to 54)
Terminal
Node 86
Class = No
Class Cases %
No 298 65.5
Yes 157 34.5
W = 455.000
N = 455
AGE_OF_VEHICLE > 16.50
Node 89
Class = No
OCCUPANT_AGE_GROUPS$ = (55 to 64)
Class Cases %
No 478 60.1
Yes 318 39.9
W = 796.000
N = 796
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 88
Class = No
AGE_OF_VEHICLE <= 16.50
Class Cases %
No 626 62.0
Yes 383 38.0
W = 1009.000
N = 1009
MODEL_YEAR_OF_VEHICLE <= 1992.50
Node 87
Class = Yes
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 765 55.2
Yes 620 44.8
W = 1385.000
N = 1385
MODEL_YEAR_OF_VEHICLE > 1992.50
Terminal
Node 87
Class = No
Class Cases %
No 3023 66.9
Yes 1498 33.1
W = 4521.000
N = 4521
YEAR_OF_ACCIDENT > 2004.50
Node 86
Class = No
MODEL_YEAR_OF_VEHICLE <= 1992.50
Class Cases %
No 3788 64.1
Yes 2118 35.9
W = 5906.000
N = 5906
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 85
Class = No
YEAR_OF_ACCIDENT <= 2004.50
Class Cases %
No 17170 72.6
Yes 6465 27.4
W = 23635.000
N = 23635
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 88
Class = No
Class Cases %
No 54887 81.6
Yes 12350 18.4
W = 67237.000
N = 67237
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 84
Class = No
OCCUPANT_AGE_GROUPS$ =
(45 to 54,55 to 64)
Class Cases %
No 72057 79.3
Yes 18815 20.7
W = 90872.000
N = 90872
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 73
Class = No
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 84097 76.2
Yes 26267 23.8
W = 110364.000
N = 110364
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 72
Class = No
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 93381 75.4
Yes 30439 24.6
W = 123820.000
N = 123820
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Terminal
Node 89
Class = No
Class Cases %
No 86853 82.6
Yes 18285 17.4
W = 105138.000
N = 105138
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Node 71
Class = No
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 180234 78.7
Yes 48724 21.3
W = 228958.000
N = 228958
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 90
Class = No
Class Cases %
No 23988 92.8
Yes 1866 7.2
W = 25854.000
N = 25854
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 70
Class = No
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 204222 80.1
Yes 50590 19.9
W = 254812.000
N = 254812
EXTRICATED_Y_N$ = (No)
Node 48
Class = No
OCCUPANT_AGE_GROUPS$ =
(0 to 11,65 to 74,
75+)
Class Cases %
No 226026 76.2
Yes 70450 23.8
W = 296476.000
N = 296476
SB_USED_Y_N$ = (Yes)
Node 47
Class = No
EXTRICATED_Y_N$ = (Yes)
Class Cases %
No 239669 70.6
Yes 99905 29.4
W = 339574.000
N = 339574
Node 1
Class = Yes
SB_USED_Y_N$ = (No)
Class Cases %
No 338443 56.8
Yes 257279 43.2
W = 595722.000
N = 595722
CART TREE TOPOGRAPHY: DETAILED NODE
DATA VIEW
44
EXTRICATED_Y_N$ = (Yes)
Terminal
Node 1
Class = Yes
Class Cases %
No 6866 17.4
Yes 32540 82.6
W = 39406.000
N = 39406
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 2
Class = Yes
Class Cases %
No 3922 32.6
Yes 8113 67.4
W = 12035.000
N = 12035
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Terminal
Node 3
Class = Yes
Class Cases %
No 33 28.2
Yes 84 71.8
W = 117.000
N = 117
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Terminal
Node 4
Class = No
Class Cases %
No 189 63.2
Yes 110 36.8
W = 299.000
N = 299
TRAVEL_SPEED_GROUP$ = (0 to 15)
Node 7
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(0 to 11,75+)
Class Cases %
No 222 53.4
Yes 194 46.6
W = 416.000
N = 416
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 6
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 4144 33.3
Yes 8307 66.7
W = 12451.000
N = 12451
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 5
Class = Yes
Class Cases %
No 102 28.3
Yes 258 71.7
W = 360.000
N = 360
OCCUPANT_AGE_GROUPS$ = (65 to 74,...)
Terminal
Node 6
Class = Yes
Class Cases %
No 1118 39.8
Yes 1692 60.2
W = 2810.000
N = 2810
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Terminal
Node 7
Class = Yes
Class Cases %
No 1685 48.6
Yes 1779 51.4
W = 3464.000
N = 3464
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 8
Class = Yes
Class Cases %
No 1 6.7
Yes 14 93.3
W = 15.000
N = 15
YEAR_OF_ACCIDENT <= 1984.50
Terminal
Node 9
Class = No
Class Cases %
No 866 60.2
Yes 573 39.8
W = 1439.000
N = 1439
YEAR_OF_ACCIDENT > 1984.50
Terminal
Node 10
Class = Yes
Class Cases %
No 309 52.8
Yes 276 47.2
W = 585.000
N = 585
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 13
Class = No
YEAR_OF_ACCIDENT <= 1984.50
Class Cases %
No 1175 58.1
Yes 849 41.9
W = 2024.000
N = 2024
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Node 12
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 1176 57.7
Yes 863 42.3
W = 2039.000
N = 2039
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 11
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 2861 52.0
Yes 2642 48.0
W = 5503.000
N = 5503
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 10
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(65 to 74,75+)
Class Cases %
No 3979 47.9
Yes 4334 52.1
W = 8313.000
N = 8313
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Terminal
Node 11
Class = Yes
Class Cases %
No 73 43.2
Yes 96 56.8
W = 169.000
N = 169
YEAR_OF_ACCIDENT <= 1987.50
Terminal
Node 12
Class = No
Class Cases %
No 452 72.2
Yes 174 27.8
W = 626.000
N = 626
OCCUPANT_AGE_GROUPS$ = (55 to 64,...)
Terminal
Node 13
Class = Yes
Class Cases %
No 32 38.1
Yes 52 61.9
W = 84.000
N = 84
OCCUPANT_AGE_GROUPS$ = (45 to 54)
Terminal
Node 14
Class = No
Class Cases %
No 43 71.7
Yes 17 28.3
W = 60.000
N = 60
YEAR_OF_ACCIDENT > 1987.50
Node 16
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(55 to 64,65 to 74)
Class Cases %
No 75 52.1
Yes 69 47.9
W = 144.000
N = 144
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 15
Class = No
YEAR_OF_ACCIDENT <= 1987.50
Class Cases %
No 527 68.4
Yes 243 31.6
W = 770.000
N = 770
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 14
Class = No
OCCUPANT_AGE_GROUPS$ =
(0 to 11,75+)
Class Cases %
No 600 63.9
Yes 339 36.1
W = 939.000
N = 939
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 9
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 4579 49.5
Yes 4673 50.5
W = 9252.000
N = 9252
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 8
Class = Yes
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 4681 48.7
Yes 4931 51.3
W = 9612.000
N = 9612
YEAR_OF_ACCIDENT <= 1989.50
Node 5
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 8825 40.0
Yes 13238 60.0
W = 22063.000
N = 22063
YEAR_OF_ACCIDENT > 1989.50
Terminal
Node 15
Class = Yes
Class Cases %
No 10251 23.1
Yes 34161 76.9
W = 44412.000
N = 44412
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 4
Class = Yes
YEAR_OF_ACCIDENT <= 1989.50
Class Cases %
No 19076 28.7
Yes 47399 71.3
W = 66475.000
N = 66475
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 16
Class = Yes
Class Cases %
No 9939 37.9
Yes 16262 62.1
W = 26201.000
N = 26201
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Terminal
Node 17
Class = Yes
Class Cases %
No 8559 36.2
Yes 15094 63.8
W = 23653.000
N = 23653
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Terminal
Node 18
Class = Yes
Class Cases %
No 3799 35.6
Yes 6862 64.4
W = 10661.000
N = 10661
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Terminal
Node 19
Class = Yes
Class Cases %
No 4842 47.4
Yes 5377 52.6
W = 10219.000
N = 10219
YEAR_OF_ACCIDENT <= 1985.50
Terminal
Node 20
Class = Yes
Class Cases %
No 1561 52.6
Yes 1409 47.4
W = 2970.000
N = 2970
YEAR_OF_ACCIDENT > 1985.50
Terminal
Node 21
Class = No
Class Cases %
No 3905 57.4
Yes 2898 42.6
W = 6803.000
N = 6803
AGE_OF_VEHICLE <= 5.50
Node 26
Class = Yes
YEAR_OF_ACCIDENT <= 1985.50
Class Cases %
No 5466 55.9
Yes 4307 44.1
W = 9773.000
N = 9773
AGE_OF_VEHICLE > 5.50
Terminal
Node 22
Class = Yes
Class Cases %
No 6862 52.9
Yes 6119 47.1
W = 12981.000
N = 12981
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 25
Class = Yes
AGE_OF_VEHICLE <= 5.50
Class Cases %
No 12328 54.2
Yes 10426 45.8
W = 22754.000
N = 22754
YEAR_OF_ACCIDENT <= 1998.50
Terminal
Node 23
Class = No
Class Cases %
No 4746 59.3
Yes 3254 40.7
W = 8000.000
N = 8000
YEAR_OF_ACCIDENT > 1998.50
Terminal
Node 24
Class = Yes
Class Cases %
No 767 55.9
Yes 604 44.1
W = 1371.000
N = 1371
MODEL_YEAR_OF_VEHICLE <= 1997.50
Node 28
Class = No
YEAR_OF_ACCIDENT <= 1998.50
Class Cases %
No 5513 58.8
Yes 3858 41.2
W = 9371.000
N = 9371
MODEL_YEAR_OF_VEHICLE > 1997.50
Terminal
Node 25
Class = Yes
Class Cases %
No 151 47.9
Yes 164 52.1
W = 315.000
N = 315
OCCUPANT_AGE_GROUPS$ = (16 to 20)
Node 27
Class = No
MODEL_YEAR_OF_VEHICLE <= 1997.50
Class Cases %
No 5664 58.5
Yes 4022 41.5
W = 9686.000
N = 9686
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 24
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(12 to 15,21 to 24,
25 to 34)
Class Cases %
No 17992 55.5
Yes 14448 44.5
W = 32440.000
N = 32440
YEAR_OF_ACCIDENT <= 2003.50
Node 23
Class = Yes
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 22834 53.5
Yes 19825 46.5
W = 42659.000
N = 42659
YEAR_OF_ACCIDENT > 2003.50
Terminal
Node 26
Class = Yes
Class Cases %
No 3453 42.1
Yes 4757 57.9
W = 8210.000
N = 8210
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 22
Class = Yes
YEAR_OF_ACCIDENT <= 2003.50
Class Cases %
No 26287 51.7
Yes 24582 48.3
W = 50869.000
N = 50869
CAR_COMPANY_HQ_REGION$ = (USA)
Node 21
Class = Yes
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 30086 48.9
Yes 31444 51.1
W = 61530.000
N = 61530
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 20
Class = Yes
CAR_COMPANY_HQ_REGION$ =
(Europe,Japan,Korea,
Other Imports)
Class Cases %
No 38645 45.4
Yes 46538 54.6
W = 85183.000
N = 85183
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 27
Class = Yes
Class Cases %
No 110 41.7
Yes 154 58.3
W = 264.000
N = 264
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 28
Class = No
Class Cases %
No 67 69.1
Yes 30 30.9
W = 97.000
N = 97
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 32
Class = Yes
TRAVEL_SPEED_GROUP$ = (16 to 35)
Class Cases %
No 177 49.0
Yes 184 51.0
W = 361.000
N = 361
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 29
Class = No
Class Cases %
No 689 64.2
Yes 385 35.8
W = 1074.000
N = 1074
YEAR_OF_ACCIDENT <= 2007.50
Node 31
Class = No
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 866 60.3
Yes 569 39.7
W = 1435.000
N = 1435
YEAR_OF_ACCIDENT > 2007.50
Terminal
Node 30
Class = Yes
Class Cases %
No 89 40.6
Yes 130 59.4
W = 219.000
N = 219
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Node 30
Class = No
YEAR_OF_ACCIDENT <= 2007.50
Class Cases %
No 955 57.7
Yes 699 42.3
W = 1654.000
N = 1654
CAR_COMPANY_HQ_REGION$ = (Other Impo...)
Terminal
Node 31
Class = No
Class Cases %
No 3202 73.1
Yes 1180 26.9
W = 4382.000
N = 4382
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 29
Class = No
CAR_COMPANY_HQ_REGION$ =
(Europe,Japan,Korea)
Class Cases %
No 4157 68.9
Yes 1879 31.1
W = 6036.000
N = 6036
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 19
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 42802 46.9
Yes 48417 53.1
W = 91219.000
N = 91219
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 32
Class = Yes
Class Cases %
No 93 34.2
Yes 179 65.8
W = 272.000
N = 272
MODEL_YEAR_OF_VEHICLE <= 1977.50
Terminal
Node 33
Class = No
Class Cases %
No 5613 66.4
Yes 2843 33.6
W = 8456.000
N = 8456
YEAR_OF_ACCIDENT <= 1985.50
Terminal
Node 34
Class = No
Class Cases %
No 2365 62.8
Yes 1400 37.2
W = 3765.000
N = 3765
OCCUPANT_AGE_GROUPS$ = (21 to 24,...)
Terminal
Node 35
Class = Yes
Class Cases %
No 1586 53.3
Yes 1391 46.7
W = 2977.000
N = 2977
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 36
Class = No
Class Cases %
No 514 64.3
Yes 286 35.8
W = 800.000
N = 800
YEAR_OF_ACCIDENT > 1985.50
Node 39
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(21 to 24,25 to 34,
35 to 44)
Class Cases %
No 2100 55.6
Yes 1677 44.4
W = 3777.000
N = 3777
MODEL_YEAR_OF_VEHICLE > 1977.50
Node 38
Class = No
YEAR_OF_ACCIDENT <= 1985.50
Class Cases %
No 4465 59.2
Yes 3077 40.8
W = 7542.000
N = 7542
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 37
Class = No
MODEL_YEAR_OF_VEHICLE <= 1977.50
Class Cases %
No 10078 63.0
Yes 5920 37.0
W = 15998.000
N = 15998
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 36
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 10171 62.5
Yes 6099 37.5
W = 16270.000
N = 16270
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 37
Class = No
Class Cases %
No 3680 72.7
Yes 1385 27.3
W = 5065.000
N = 5065
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 35
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 13851 64.9
Yes 7484 35.1
W = 21335.000
N = 21335
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 38
Class = No
Class Cases %
No 1328 84.6
Yes 241 15.4
W = 1569.000
N = 1569
YEAR_OF_ACCIDENT <= 1993.50
Node 34
Class = No
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 15179 66.3
Yes 7725 33.7
W = 22904.000
N = 22904
MODEL_YEAR_OF_VEHICLE <= 1976.50
Terminal
Node 39
Class = No
Class Cases %
No 80 61.5
Yes 50 38.5
W = 130.000
N = 130
MODEL_YEAR_OF_VEHICLE > 1976.50
Terminal
Node 40
Class = Yes
Class Cases %
No 947 37.5
Yes 1575 62.5
W = 2522.000
N = 2522
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 42
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 1976.50
Class Cases %
No 1027 38.7
Yes 1625 61.3
W = 2652.000
N = 2652
MODEL_YEAR_OF_VEHICLE <= 1979.50
Terminal
Node 41
Class = No
Class Cases %
No 357 64.8
Yes 194 35.2
W = 551.000
N = 551
YEAR_OF_ACCIDENT <= 1995.50
Terminal
Node 42
Class = No
Class Cases %
No 321 59.3
Yes 220 40.7
W = 541.000
N = 541
YEAR_OF_ACCIDENT > 1995.50
Terminal
Node 43
Class = Yes
Class Cases %
No 691 52.0
Yes 638 48.0
W = 1329.000
N = 1329
MODEL_YEAR_OF_VEHICLE > 1979.50
Node 46
Class = Yes
YEAR_OF_ACCIDENT <= 1995.50
Class Cases %
No 1012 54.1
Yes 858 45.9
W = 1870.000
N = 1870
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 45
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 1979.50
Class Cases %
No 1369 56.5
Yes 1052 43.5
W = 2421.000
N = 2421
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 44
Class = No
Class Cases %
No 257 66.8
Yes 128 33.2
W = 385.000
N = 385
YEAR_OF_ACCIDENT <= 2000.50
Node 44
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 1626 57.9
Yes 1180 42.1
W = 2806.000
N = 2806
YEAR_OF_ACCIDENT > 2000.50
Terminal
Node 45
Class = Yes
Class Cases %
No 1821 46.8
Yes 2067 53.2
W = 3888.000
N = 3888
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 43
Class = Yes
YEAR_OF_ACCIDENT <= 2000.50
Class Cases %
No 3447 51.5
Yes 3247 48.5
W = 6694.000
N = 6694
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 41
Class = Yes
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 4474 47.9
Yes 4872 52.1
W = 9346.000
N = 9346
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 46
Class = No
Class Cases %
No 438 73.4
Yes 159 26.6
W = 597.000
N = 597
YEAR_OF_ACCIDENT > 1993.50
Node 40
Class = Yes
TRAVEL_SPEED_GROUP$ = (36 to 55)
Class Cases %
No 4912 49.4
Yes 5031 50.6
W = 9943.000
N = 9943
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 33
Class = No
YEAR_OF_ACCIDENT <= 1993.50
Class Cases %
No 20091 61.2
Yes 12756 38.8
W = 32847.000
N = 32847
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Node 18
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 62893 50.7
Yes 61173 49.3
W = 124066.000
N = 124066
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 17
Class = Yes
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 72832 48.5
Yes 77435 51.5
W = 150267.000
N = 150267
EXTRICATED_Y_N$ = (No)
Node 3
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(0 to 11,45 to 54,
55 to 64,65 to 74,
75+)
Class Cases %
No 91908 42.4
Yes 124834 57.6
W = 216742.000
N = 216742
SB_USED_Y_N$ = (No)
Node 2
Class = Yes
EXTRICATED_Y_N$ = (Yes)
Class Cases %
No 98774 38.6
Yes 157374 61.4
W = 256148.000
N = 256148
EXTRICATED_Y_N$ = (Yes)
Terminal
Node 47
Class = Yes
Class Cases %
No 13643 31.7
Yes 29455 68.3
W = 43098.000
N = 43098
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Terminal
Node 48
Class = Yes
Class Cases %
No 3290 36.3
Yes 5770 63.7
W = 9060.000
N = 9060
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 49
Class = Yes
Class Cases %
No 4541 46.0
Yes 5334 54.0
W = 9875.000
N = 9875
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 50
Class = No
Class Cases %
No 474 63.9
Yes 268 36.1
W = 742.000
N = 742
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 51
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 5015 47.2
Yes 5602 52.8
W = 10617.000
N = 10617
OCCUPANT_AGE_GROUPS$ = (75+)
Node 50
Class = Yes
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 8305 42.2
Yes 11372 57.8
W = 19677.000
N = 19677
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Terminal
Node 51
Class = Yes
Class Cases %
No 612 38.3
Yes 987 61.7
W = 1599.000
N = 1599
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Terminal
Node 52
Class = Yes
Class Cases %
No 857 50.8
Yes 831 49.2
W = 1688.000
N = 1688
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 53
Class = No
Class Cases %
No 108 64.3
Yes 60 35.7
W = 168.000
N = 168
SEX$ = (Female)
Node 57
Class = Yes
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 965 52.0
Yes 891 48.0
W = 1856.000
N = 1856
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 54
Class = Yes
Class Cases %
No 127 53.4
Yes 111 46.6
W = 238.000
N = 238
AGE_OF_VEHICLE <= 6.50
Terminal
Node 55
Class = No
Class Cases %
No 151 61.9
Yes 93 38.1
W = 244.000
N = 244
AGE_OF_VEHICLE > 6.50
Terminal
Node 56
Class = Yes
Class Cases %
No 104 48.6
Yes 110 51.4
W = 214.000
N = 214
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Node 60
Class = Yes
AGE_OF_VEHICLE <= 6.50
Class Cases %
No 255 55.7
Yes 203 44.3
W = 458.000
N = 458
CAR_COMPANY_HQ_REGION$ = (Europe,...)
Terminal
Node 57
Class = No
Class Cases %
No 1818 64.9
Yes 985 35.1
W = 2803.000
N = 2803
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 59
Class = No
CAR_COMPANY_HQ_REGION$ =
(Japan,Other Imports)
Class Cases %
No 2073 63.6
Yes 1188 36.4
W = 3261.000
N = 3261
SEX$ = (*,Male)
Node 58
Class = No
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 2200 62.9
Yes 1299 37.1
W = 3499.000
N = 3499
YEAR_OF_ACCIDENT <= 2003.50
Node 56
Class = No
SEX$ = (Female)
Class Cases %
No 3165 59.1
Yes 2190 40.9
W = 5355.000
N = 5355
YEAR_OF_ACCIDENT > 2003.50
Terminal
Node 58
Class = Yes
Class Cases %
No 825 47.1
Yes 925 52.9
W = 1750.000
N = 1750
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 55
Class = Yes
YEAR_OF_ACCIDENT <= 2003.50
Class Cases %
No 3990 56.2
Yes 3115 43.8
W = 7105.000
N = 7105
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 54
Class = Yes
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 4602 52.9
Yes 4102 47.1
W = 8704.000
N = 8704
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 59
Class = No
Class Cases %
No 407 76.5
Yes 125 23.5
W = 532.000
N = 532
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 53
Class = Yes
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 5009 54.2
Yes 4227 45.8
W = 9236.000
N = 9236
TRAVEL_SPEED_GROUP$ = (> 75)
Terminal
Node 60
Class = Yes
Class Cases %
No 23 41.8
Yes 32 58.2
W = 55.000
N = 55
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Terminal
Node 61
Class = No
Class Cases %
No 4383 68.0
Yes 2060 32.0
W = 6443.000
N = 6443
AGE_OF_VEHICLE <= 8.50
Node 64
Class = No
TRAVEL_SPEED_GROUP$ = (> 75)
Class Cases %
No 4406 67.8
Yes 2092 32.2
W = 6498.000
N = 6498
YEAR_OF_ACCIDENT <= 1992.50
Terminal
Node 62
Class = No
Class Cases %
No 166 72.2
Yes 64 27.8
W = 230.000
N = 230
MODEL_YEAR_OF_VEHICLE <= 2001.50
Terminal
Node 63
Class = Yes
Class Cases %
No 313 47.9
Yes 340 52.1
W = 653.000
N = 653
MODEL_YEAR_OF_VEHICLE > 2001.50
Terminal
Node 64
Class = No
Class Cases %
No 272 58.6
Yes 192 41.4
W = 464.000
N = 464
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Node 68
Class = Yes
MODEL_YEAR_OF_VEHICLE <= 2001.50
Class Cases %
No 585 52.4
Yes 532 47.6
W = 1117.000
N = 1117
TRAVEL_SPEED_GROUP$ = (16 to 35)
Terminal
Node 65
Class = No
Class Cases %
No 73 62.9
Yes 43 37.1
W = 116.000
N = 116
SEX$ = (*,Female)
Node 67
Class = Yes
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 658 53.4
Yes 575 46.6
W = 1233.000
N = 1233
AGE_OF_VEHICLE <= 14.50
Terminal
Node 66
Class = No
Class Cases %
No 1068 61.2
Yes 677 38.8
W = 1745.000
N = 1745
AGE_OF_VEHICLE > 14.50
Terminal
Node 67
Class = Yes
Class Cases %
No 336 54.8
Yes 277 45.2
W = 613.000
N = 613
SEX$ = (Male)
Node 69
Class = No
AGE_OF_VEHICLE <= 14.50
Class Cases %
No 1404 59.5
Yes 954 40.5
W = 2358.000
N = 2358
YEAR_OF_ACCIDENT > 1992.50
Node 66
Class = No
SEX$ = (*,Female)
Class Cases %
No 2062 57.4
Yes 1529 42.6
W = 3591.000
N = 3591
AGE_OF_VEHICLE > 8.50
Node 65
Class = No
YEAR_OF_ACCIDENT <= 1992.50
Class Cases %
No 2228 58.3
Yes 1593 41.7
W = 3821.000
N = 3821
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 63
Class = No
AGE_OF_VEHICLE <= 8.50
Class Cases %
No 6634 64.3
Yes 3685 35.7
W = 10319.000
N = 10319
VEHICLE_WEIGHT_GROUP$ = (3951 to 47...)
Terminal
Node 68
Class = No
Class Cases %
No 1237 72.7
Yes 465 27.3
W = 1702.000
N = 1702
TRAVEL_SPEED_GROUP$ = (16 to 35,...)
Node 62
Class = No
VEHICLE_WEIGHT_GROUP$ =
(3201 to 3950)
Class Cases %
No 7871 65.5
Yes 4150 34.5
W = 12021.000
N = 12021
TRAVEL_SPEED_GROUP$ = (0 to 15)
Terminal
Node 69
Class = No
Class Cases %
No 619 84.8
Yes 111 15.2
W = 730.000
N = 730
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Node 61
Class = No
TRAVEL_SPEED_GROUP$ =
(16 to 35,36 to 55,
56 to 75,> 75)
Class Cases %
No 8490 66.6
Yes 4261 33.4
W = 12751.000
N = 12751
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 52
Class = No
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 13499 61.4
Yes 8488 38.6
W = 21987.000
N = 21987
OCCUPANT_AGE_GROUPS$ = (0 to 11,...)
Node 49
Class = Yes
OCCUPANT_AGE_GROUPS$ = (75+)
Class Cases %
No 21804 52.3
Yes 19860 47.7
W = 41664.000
N = 41664
TRAVEL_SPEED_GROUP$ = (56 to 75,...)
Terminal
Node 70
Class = No
Class Cases %
No 9284 69.0
Yes 4172 31.0
W = 13456.000
N = 13456
YEAR_OF_ACCIDENT <= 1980.50
Terminal
Node 71
Class = No
Class Cases %
No 43 70.5
Yes 18 29.5
W = 61.000
N = 61
AGE_OF_VEHICLE <= 5.50
Terminal
Node 72
Class = No
Class Cases %
No 368 58.0
Yes 266 42.0
W = 634.000
N = 634
AGE_OF_VEHICLE > 5.50
Terminal
Node 73
Class = Yes
Class Cases %
No 38 38.4
Yes 61 61.6
W = 99.000
N = 99
AGE_OF_VEHICLE <= 6.50
Node 78
Class = Yes
AGE_OF_VEHICLE <= 5.50
Class Cases %
No 406 55.4
Yes 327 44.6
W = 733.000
N = 733
AGE_OF_VEHICLE > 6.50
Terminal
Node 74
Class = No
Class Cases %
No 162 63.0
Yes 95 37.0
W = 257.000
N = 257
YEAR_OF_ACCIDENT > 1980.50
Node 77
Class = No
AGE_OF_VEHICLE <= 6.50
Class Cases %
No 568 57.4
Yes 422 42.6
W = 990.000
N = 990
YEAR_OF_ACCIDENT <= 1990.50
Node 76
Class = No
YEAR_OF_ACCIDENT <= 1980.50
Class Cases %
No 611 58.1
Yes 440 41.9
W = 1051.000
N = 1051
YEAR_OF_ACCIDENT > 1990.50
Terminal
Node 75
Class = Yes
Class Cases %
No 1386 46.7
Yes 1584 53.3
W = 2970.000
N = 2970
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 75
Class = Yes
YEAR_OF_ACCIDENT <= 1990.50
Class Cases %
No 1997 49.7
Yes 2024 50.3
W = 4021.000
N = 4021
MODEL_YEAR_OF_VEHICLE <= 1988.50
Terminal
Node 76
Class = No
Class Cases %
No 954 63.5
Yes 548 36.5
W = 1502.000
N = 1502
MODEL_YEAR_OF_VEHICLE <= 1994.50
Terminal
Node 77
Class = Yes
Class Cases %
No 485 53.7
Yes 419 46.3
W = 904.000
N = 904
MODEL_YEAR_OF_VEHICLE > 1994.50
Terminal
Node 78
Class = No
Class Cases %
No 331 64.6
Yes 181 35.4
W = 512.000
N = 512
MODEL_YEAR_OF_VEHICLE > 1988.50
Node 82
Class = No
MODEL_YEAR_OF_VEHICLE <= 1994.50
Class Cases %
No 816 57.6
Yes 600 42.4
W = 1416.000
N = 1416
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Node 81
Class = No
MODEL_YEAR_OF_VEHICLE <= 1988.50
Class Cases %
No 1770 60.7
Yes 1148 39.3
W = 2918.000
N = 2918
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 79
Class = No
Class Cases %
No 7443 67.4
Yes 3598 32.6
W = 11041.000
N = 11041
AGE_OF_VEHICLE <= 13.50
Node 80
Class = No
OCCUPANT_AGE_GROUPS$ = (35 to 44)
Class Cases %
No 9213 66.0
Yes 4746 34.0
W = 13959.000
N = 13959
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 80
Class = Yes
Class Cases %
No 584 52.1
Yes 536 47.9
W = 1120.000
N = 1120
OCCUPANT_AGE_GROUPS$ = (16 to 20)
Terminal
Node 81
Class = No
Class Cases %
No 246 62.8
Yes 146 37.2
W = 392.000
N = 392
AGE_OF_VEHICLE > 13.50
Node 83
Class = Yes
OCCUPANT_AGE_GROUPS$ =
(12 to 15,21 to 24,
25 to 34,35 to 44)
Class Cases %
No 830 54.9
Yes 682 45.1
W = 1512.000
N = 1512
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 79
Class = No
AGE_OF_VEHICLE <= 13.50
Class Cases %
No 10043 64.9
Yes 5428 35.1
W = 15471.000
N = 15471
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Node 74
Class = No
OCCUPANT_AGE_GROUPS$ =
(45 to 54,55 to 64)
Class Cases %
No 12040 61.8
Yes 7452 38.2
W = 19492.000
N = 19492
YEAR_OF_ACCIDENT <= 2004.50
Terminal
Node 82
Class = No
Class Cases %
No 13382 75.5
Yes 4347 24.5
W = 17729.000
N = 17729
CAR_COMPANY_HQ_REGION$ = (Japan,...)
Terminal
Node 83
Class = Yes
Class Cases %
No 139 37.0
Yes 237 63.0
W = 376.000
N = 376
AGE_OF_VEHICLE <= 16.50
Terminal
Node 84
Class = No
Class Cases %
No 148 69.5
Yes 65 30.5
W = 213.000
N = 213
OCCUPANT_AGE_GROUPS$ = (55 to 64)
Terminal
Node 85
Class = Yes
Class Cases %
No 180 52.8
Yes 161 47.2
W = 341.000
N = 341
OCCUPANT_AGE_GROUPS$ = (45 to 54)
Terminal
Node 86
Class = No
Class Cases %
No 298 65.5
Yes 157 34.5
W = 455.000
N = 455
AGE_OF_VEHICLE > 16.50
Node 89
Class = No
OCCUPANT_AGE_GROUPS$ = (55 to 64)
Class Cases %
No 478 60.1
Yes 318 39.9
W = 796.000
N = 796
CAR_COMPANY_HQ_REGION$ = (Europe,USA)
Node 88
Class = No
AGE_OF_VEHICLE <= 16.50
Class Cases %
No 626 62.0
Yes 383 38.0
W = 1009.000
N = 1009
MODEL_YEAR_OF_VEHICLE <= 1992.50
Node 87
Class = Yes
CAR_COMPANY_HQ_REGION$ =
(Japan,Korea,Other Imports)
Class Cases %
No 765 55.2
Yes 620 44.8
W = 1385.000
N = 1385
MODEL_YEAR_OF_VEHICLE > 1992.50
Terminal
Node 87
Class = No
Class Cases %
No 3023 66.9
Yes 1498 33.1
W = 4521.000
N = 4521
YEAR_OF_ACCIDENT > 2004.50
Node 86
Class = No
MODEL_YEAR_OF_VEHICLE <= 1992.50
Class Cases %
No 3788 64.1
Yes 2118 35.9
W = 5906.000
N = 5906
OCCUPANT_AGE_GROUPS$ = (45 to 54,...)
Node 85
Class = No
YEAR_OF_ACCIDENT <= 2004.50
Class Cases %
No 17170 72.6
Yes 6465 27.4
W = 23635.000
N = 23635
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Terminal
Node 88
Class = No
Class Cases %
No 54887 81.6
Yes 12350 18.4
W = 67237.000
N = 67237
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 84
Class = No
OCCUPANT_AGE_GROUPS$ =
(45 to 54,55 to 64)
Class Cases %
No 72057 79.3
Yes 18815 20.7
W = 90872.000
N = 90872
TRAVEL_SPEED_GROUP$ = (36 to 55)
Node 73
Class = No
VEHICLE_WEIGHT_GROUP$ = (< 2451)
Class Cases %
No 84097 76.2
Yes 26267 23.8
W = 110364.000
N = 110364
VEHICLE_WEIGHT_GROUP$ = (2451 to 32...)
Node 72
Class = No
TRAVEL_SPEED_GROUP$ =
(56 to 75,> 75)
Class Cases %
No 93381 75.4
Yes 30439 24.6
W = 123820.000
N = 123820
VEHICLE_WEIGHT_GROUP$ = (3201 to 39...)
Terminal
Node 89
Class = No
Class Cases %
No 86853 82.6
Yes 18285 17.4
W = 105138.000
N = 105138
TRAVEL_SPEED_GROUP$ = (36 to 55,...)
Node 71
Class = No
VEHICLE_WEIGHT_GROUP$ =
(2451 to 3200,< 2451)
Class Cases %
No 180234 78.7
Yes 48724 21.3
W = 228958.000
N = 228958
TRAVEL_SPEED_GROUP$ = (0 to 15,...)
Terminal
Node 90
Class = No
Class Cases %
No 23988 92.8
Yes 1866 7.2
W = 25854.000
N = 25854
OCCUPANT_AGE_GROUPS$ = (12 to 15,...)
Node 70
Class = No
TRAVEL_SPEED_GROUP$ =
(36 to 55,56 to 75,
> 75)
Class Cases %
No 204222 80.1
Yes 50590 19.9
W = 254812.000
N = 254812
EXTRICATED_Y_N$ = (No)
Node 48
Class = No
OCCUPANT_AGE_GROUPS$ =
(0 to 11,65 to 74,
75+)
Class Cases %
No 226026 76.2
Yes 70450 23.8
W = 296476.000
N = 296476
SB_USED_Y_N$ = (Yes)
Node 47
Class = No
EXTRICATED_Y_N$ = (Yes)
Class Cases %
No 239669 70.6
Yes 99905 29.4
W = 339574.000
N = 339574
Node 1
Class = Yes
SB_USED_Y_N$ = (No)
Class Cases %
No 338443 56.8
Yes 257279 43.2
W = 595722.000
N = 595722
CART TREE TOPOGRAPHY: DETAILED NODE
DATA VIEW
45
HOTSPOT REPORT: TREE NODE #S WITH
THE HIGHEST % OF FATALITIES
46
HOTSPOT REPORT: TREE NODE #S WITH
THE LOWEST % OF FATALITIES
47
MARS (MULTIVARIATE ADAPTIVE REGRESSION
SPLINES) AUTOMATED REGRESSION MODELING
With missing value (mis) importance rankings
Variable Importance rankings as part of the analysis summary:
48Fatality rates increase as vehicles age
High vehicle
age
TREENET AUTOMATED 3D CHARTING OF
ALL POSSIBLE FACTOR COMBINATIONS
Treenet
3D charts
Datafit charts with RSq & p-values for the 3D surfaces
Fatality rates increase 70% with higher vehicle age +
occupant age vs. new vehicles and young occupants
High vehicle +
Occupant age
49
UNSUPERVISED VEHICLE ACCIDENT
RESEARCH: SEEKING FOR NEW THEORIES
• Steel and other materials lose
50%+ of their strength over time
• Corrosion adds to crash risks
• Aging cars collapse more in a
crash as they age
• Older passengers and / or aging
cars are a risky mix
• Heavy vehicles are generally safer
• Vehicle Star ratings fade over time
• Multiple confounding factors
decide who lives and who dies
• 18 other high income countries
have lower traffic accident death
rates than the USA. Why?
Research Question:
“What’s going on here?”
50
CONCLUSION
• Robust applications of ML
techniques can safeguard
against superficial analysis
and analysis paralysis
• ML can offer the problem-
solvers new guidance and
hotspot detection with its
automated deep analysis
and exhaustive data
stratification
• In unsupervised analysis
mode, ML can offer new
insights and theories for
future research
consideration
51
Q & A
Any Questions?
Author contact information:
David.Patrishkoff@CascadeEffects.com

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Lss 103 patrishkoff_the application of ml tools on complex and big data projects_ppt

  • 1. The Application of Machine Learning Tools on Complex and Big Data Projects David J Patrishkoff
  • 2. 2 LSS Track #10 Session #LSS-103 The Application of Machine Learning Tools on Complex and Big Data Projects 9:15 AM – 9:50 AM March 14th, 2019 LSS WORLD CONFERENCE David J Patrishkoff
  • 3. 3 INTRODUCTION Each of the Google search terms “Machine Learning” (ML), and “Artificial Intelligence” (AI) are now more popular in the USA than “Six Sigma” and “Lean Manufacturing” combined. ML is a branch of AI. The advantages of ML analysis applications for complex problem-solving, data mining, and research projects will be presented.
  • 5. 5 LEARNING POINT TOPICS 1. LSS analysis tools - upgrades available 2. Overcoming superficial analysis & analysis-paralysis risks 3. ML applications for complex and big data projects
  • 6. 1. LSS ANALYSIS TOOLS – UPGRADES AVAILABLE
  • 7. 7 The greatest obstacle to discovery is not ignorance – It is the illusion of knowledge Daniel J Boorstin Scholar When you apply computer science and machine learning to areas that haven't had any innovation in 50 years, you can make rapid advances that seem really incredible Bill Maris Founder and first CEO of Google Ventures The biggest room in the world is the room for improvement Helmut Schmidt German Politician NEVER STOP IMPROVING
  • 8. 8 LSS ANALYSIS UPGRADES ARE AVAILABLE • Tech upgrades often come faster than expected • Continually assess new options to be more effective • Be a life-long learner • Don’t become obsoleted, one upgrade at a time… WARNING! You have ignored 35,476 Upgrades
  • 9. 9 SIGNS THAT YOUR DATA COMPLEXITY HAS OUTGROWN YOUR SOFTWARE CAPABILITY • Analysis software often freezes & crashes • Data stratification limits are impeding your analysis • Unrestricted non-linear analysis is needed • You need to find hidden pockets of factor interactions • Project complexity is driving circular analysis • You cannot find the sweet spot between simplicity and analysis paralysis
  • 10. 2. OVERCOMING SUPERFICIAL ANALYSIS & ANALYSIS-PARALYSIS RISKS
  • 11. 11 DRIVERS OF SUPERFICIAL ANALYSIS Shallow exploration efforts Time constraintsUnderestimating complexity
  • 12. 12 OTHER SUPERFICIAL ANALYSIS RISKS • Shortcuts = Analysis, professional & reputational risks • Just exploring the blindingly obvious • Using weak analysis tools to address a complex problem • Substituting process & methods with instincts and impulsiveness • Applying random or inefficient strategies for analysis Only strong characters can resist the temptation of superficial analysis. Albert Einstein
  • 13. 13 ADDRESSING COMPLEXITY WITH KISS K e e p I t S u p e r S i m p l e • KISS is the ultimate goal for data analysis • KISS makes everyone happy • KI$$ reduces analysis time and costs • KISS is tough to implement
  • 14. 14 KISS ANALYSIS SHOULD INCLUDE ASPECTS OF ERROR-PROOFING • Error-proofed software analysis should include: • Ease of use • Intuitive operations • Analysis wizard support • Video instruction availability • Easy access to experts and help desks • Checklists for sequential analysis steps • Automated & exhaustive charting of all data • Automated analysis interpretation comments
  • 15. 15 Superficial Analysis Analysis Paralysis An Effective Data Analysis Strategy Shallow analysis Circular analysis CREATING AN EFFECTIVE STRATEGY OF ANALYSIS (SOA) a written analysis strategy with sequentially listed analysis tasks
  • 16. 16 THE PURSUIT OF ANALYSIS WITHOUT PARALYSIS • Analysis without a plan can lead to paralysis. • Don’t get so consumed with solving a problem that you forget to solve the problem • Machine Learning and Neural Network analysis software are quite simple… but it just takes a genius to understand their simplicity The only simplicity to be trusted is the simplicity to be found on the far side of complexity Alfred North Whitehead (English mathematician and philosopher)
  • 17. 3. ML APPLICATIONS FOR COMPLEX AND BIG DATA PROJECTS
  • 18. 18 WHEN YOUR PROJECT DATA OUTGROWS YOUR ANALYSIS SOFTWARE CAPABILITY Options: • Narrow the project scope • Limit the number of factors in the study • Transfer the analysis to 64 Bit / high data capability software • Explore the capabilities of high data capability machine learning software • Write code for your own analysis
  • 19. 19 Small (100s - millions) Logscale for project complexity  Project data description (from a Big Data perspective) Classic LSS problem- solving Big Data analysis and AI solutions THE EVOLUTION OF BUSINESS DATA AND INFORMATION ANALYSIS  Big Data (1TB++) (trillions++ of data fields) Analysis Capability Learning Opportunity for LSS Belts Medium (billions)   Data Scientists, data miners, social media miners, quants, AI engineers, etc. WBs, YBs, GBs, BBs & MBBs      
  • 20. 20 SOME OF THE ANALYSIS CAPABILITIES AVAILABLE FOR COMPLEX LSS PROJECTS • Advanced statistical analysis • Automated non-linear analysis • Regression and classification trees with hotspot detection • Model factor importance rankings • Adjustment for confounding factors • Automated charting of all possible 2D & 3D plots 64 Bit SW Predictive Modeling Data Mining / Research Problem-Solving • Supervised & unsupervised analysis modes 32 Bit SW • Automated data binning suggestions • 64 bit large data handling capability • Predictive Modeling
  • 21. 21 SCOPE OF ANALYSIS FOR MY TRANSPORTATION SAFETY RESEARCH • 1975 to 2017 CY traffic accidents that involved at least 1 fatality in a vehicle • Data available for all survivors and victims • Analyzed over 300 scientific accident research articles • Over 1.3 billion fields of data available in US FARS data (Fatality Analysis Reporting System) • Discover why 18 other high income countries have lower traffic fatality rates than the USA Two of the many road-side shrines for vehicle accident victims
  • 22. 22 SOFTWARE SUPPORTING MY TRANSPORTATION SAFETY RESEARCH from Minitab Machine learning software From Oakdale Engineering Non-linear Multiple Variable Regression Research Project Mind Mapping and Planning Also offers R-Sq & P-Values for non-linear 3D regression surfaces Statistical analysis software
  • 23. 23 MY TRANSPORTATION SAFETY RESEARCH GOAL Discover the interacting factors that result in non-injuries, injury, or death in serious vehicle crashes
  • 24. 24 CRASHWORTHINESS: THE VEHICLES ABILITY TO PROTECT ITS OCCUPANTS IN ANY ACCIDENT Some standard impact types: Frontal impact Side or angle impact Tree or pole impact Rear impact Rollovers
  • 25. 25 1975 / 2018 MY Chevy Impala 1975 / 2018 MY Ford Pickup Truck 1975 / 2018 MY Chrysler Van 1975 / 2018 MY Jeep Cherokee RESEARCH SCOPE: ALL PASSENGER VEHICLE FATAL ACCIDENTS FROM 1975 TO PRESENT
  • 26. 26 RESEARCH QUESTION: HOW FAST AND BY HOW MUCH DOES A NHTSA STAR FADE? NHTSA (National Highway Traffic Safety Administration) NHTSA’s 5-Star Vehicle Safety Ratings Program Ratings standards have changed and expanded over the years Vehicle structures weaken over time due to material fatigue and corrosion which lessons their ability to absorb energy during a crash
  • 27. 27 HISTORICAL TREND: THE AGING USA VEHICLE FLEET ON THE ROADS 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 13 12 11 10 9 8 7 6 5 4 Year of Accident MeanofAgeofVehicle 4 7 11 13 Passenger Cars Pickups Utility Vehicles Vans Vehicle Body Type Age of Passenger Vehicles on US Roads
  • 28. 28 HISTORICAL TREND: SEAT BELT USAGE 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 90 80 70 60 50 40 30 20 10 0 Year of Accident MeanofPercentSeatBeltUsage 0 80 70 10 Passenger Cars Pickups Utility Vehicles Vans Vehicle Body Type % Seat Belt Usage by Passenger Vehicle Type
  • 29. 29 IDENTIFYING THE OPTIMAL BIN SIZE AND CUT-OFF VALUES SPM can identify the ideal min and max bin values for continuous values that will be statistically analyzed in stratified groupings. Example: Age of Vehicle.
  • 30. 30 20+191817161514131211109876543210 55 50 45 40 35 30 25 20 Age of Vehicle PercentKilled 23.6769 27.0855 34.661 35.6656 36.656636.6186 42.80943.051 45.3231 49.6786 50.8082 49.2294 26.2783 52.9058 27.698 28.835728.2635 29.8149 32.5149 30.7062 34.1622 Interval Plot of Percent Killed vs Age of Vehicle 95% CI for the Mean Individual standard deviations are used to calculate the intervals. for belted drivers of passenger cars - 1978 - 2017 CY NON-OPTIMIZED BINNING OF VEHICLE AGE n = 79,082
  • 31. 31 OPTIMIZED BINNING OF VEHICLE AGE 16 to 20+13 to 1511 to 129 to 107 to 865430 to 2 50 45 40 35 30 25 10 age of Vehicle Groups PercentKilled 26.5412 27.698 28.8357 28.2635 29.8149 31.611 34.4141 36.1481 40.4395 50.0487 Interval Plot of Percent Killed vs 10 age of Vehicle Groups 95% CI for the Mean Individual standard deviations are used to calculate the intervals. for belted drivers of passenger cars - 1978 - 2017 CY n = 79,082 The optimized binning group sizes resulted in groups with more consistent confidence interval ranges
  • 32. 32 75+65 - 7455 - 6445 - 5435 - 4425 - 3421 - 2416 - 2012 - 150 - 11 200 150 100 50 0 -50 -100 Occupant Age Groups PercentKilled 62.741 46.609639.716834.554730.204127.429528.106127.6687 25.7576 33.3333 Interval Plot of Percent Killed 95% CI for the Mean Individual standard deviations are used to calculate the intervals. for belted drivers of passenger cars - 1978 to 2017 CY NON-OPTIMIZED BINNING OF OCCUPANT AGES
  • 33. 33 OPTIMIZED BINNING OF OCCUPANT AGES 72++59 to 7149 to 5841 to 4835 to 4030 to 3426 to 2922 to 2519 to 210 to 18 65 60 55 50 45 40 35 30 25 10 Occupant Age Groups PercentKilled 60.2062 42.8872 36.7335 32.1771 29.199728.428 26.771227.646627.7219 27.725 Interval Plot of Percent Killed 95% CI for the Mean Individual standard deviations are used to calculate the intervals. for belted drivers of passenger cars - 1978 to 2017 CY The optimized binning group sizes resulted in groups with more consistent confidence interval ranges
  • 34. 34 NON-OPTIMIZED BINNING OF VEHICLE WEIGHT 4701+3951 - 47003201 - 39502451 - 3200Up to 2450 50 40 30 20 10 0 Vehicle Weight Group PercentKilled 9.04762 20.9455 26.662 35.6063 45.6874 Interval Plot of Percent Killed 95% CI for the Mean Individual standard deviations are used to calculate the intervals. for belted drivers of passenger cars - 1978 to 2017 CY
  • 35. 35 OPTIMIZED BINNING OF VEHICLE WEIGHT 3778++ 3455 to 3777 3261 to 3454 3110 to 3260 2951 to 3109 2723 to 2950 2553 to 2722 2404 to 2552 2181 to 2403 Under 2180 50 45 40 35 30 25 20 10 Vehicle Weight Groups PercentKilled 20.852 25.5846 27.7143 31.3654 30.5886 35.3399 39.4967 41.521 43.4374 48.994 Interval Plot of Percent Killed 95% CI for the Mean Individual standard deviations are used to calculate the intervals. for belted drivers of passenger cars - 1978 to 2017 Optimized binning = more consistent confidence interval ranges
  • 36. 36 SCOPE: 160 CRASHWORTHINESS STUDIES INTO WHO SURVIVES SERIOUS ACCIDENTS Selected Machine learning analysis outputs will be shown for 1 of 160 studies: Driver safety for frontal impacts in passenger cars Machine learning analysis can conduct all 160 studies at the same time and automatically sort out the safest and riskiest occupant conditions for every significant stratification groups
  • 37. 37 Lower Fatalities (Dark blue boxes) Higher Fatalities (Dark red boxes) CART TREE TOPOGRAPHY: TOP-LEVEL COLOR CODED SPLITTING RESULTS Smaller than optimal-sized tree split example - 90 nodes
  • 38. 38 Lower Fatalities (Dark blue boxes) Higher Fatalities (Dark red boxes) 76.9% Fatalities: No SB worn, > 1990 accident, no extrication, all ages except 12-44 YO, n = 44,412 82.6% Fatalities: No SB worn + extrication + 36- 55mph speed, n = 39,406 CART TREE TOPOGRAPHY: TOP-LEVEL COLOR CODED SPLITTING RESULTS
  • 39. 39 Lower Fatalities (Dark blue boxes) Higher Fatalities (Dark red boxes) 92.8% Non-Fatalities: SB worn, no extrication, <36 mph speed, < 65 years of occupant age N=25,854 CART TREE TOPOGRAPHY: TOP-LEVEL COLOR CODED SPLITTING RESULTS
  • 40. 40 Lower Fatalities (Dark blue boxes) Higher Fatalities (Dark red boxes) 84.8% Non-Fatalities: SB worn, no extrication, <15 mph speed, all occ ages except 12-64 YO, vehicle weight >3200 lbs, n=730 CART TREE TOPOGRAPHY: TOP-LEVEL COLOR CODED SPLITTING RESULTS
  • 41. 41 OCCUPANT_AGE_GROUPS$ TRAVEL_SPEED_GROUP$ YEAR_OF_ACCIDENT CAR_COMPANY_HQ_REGION$ VEHICLE_WEIGHT_GROUP$ OCCUPANT_AGE_GROUPS$ OCCUPANT_AGE_GROUPS$ YEAR_OF_ACCIDENT OCCUPANT_AGE_GROUPS$ TRAVEL_SPEED_GROUP$ TRAVEL_SPEED_GROUP$ VEHICLE_WEIGHT_GROUP$ YEAR_OF_ACCIDENT YEAR_OF_ACCIDENT AGE_OF_VEHICLE YEAR_OF_ACCIDENT MODEL_YEAR_OF_VEHICLE OCCUPANT_AGE_GROUPS$ OCCUPANT_AGE_GROUPS$ YEAR_OF_ACCIDENT VEHICLE_WEIGHT_GROUP$ CAR_COMPANY_HQ_REGION$ TRAVEL_SPEED_GROUP$ OCCUPANT_AGE_GROUPS$ YEAR_OF_ACCIDENT CAR_COMPANY_HQ_REGION$ TRAVEL_SPEED_GROUP$ OCCUPANT_AGE_GROUPS$ YEAR_OF_ACCIDENT MODEL_YEAR_OF_VEHICLE CAR_COMPANY_HQ_REGION$ VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ MODEL_YEAR_OF_VEHICLE YEAR_OF_ACCIDENT MODEL_YEAR_OF_VEHICLE VEHICLE_WEIGHT_GROUP$ YEAR_OF_ACCIDENT OCCUPANT_AGE_GROUPS$ TRAVEL_SPEED_GROUP$ YEAR_OF_ACCIDENT VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ OCCUPANT_AGE_GROUPS$ EXTRICATED_Y_N$ TRAVEL_SPEED_GROUP$ VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ AGE_OF_VEHICLE CAR_COMPANY_HQ_REGION$ TRAVEL_SPEED_GROUP$ SEX$ YEAR_OF_ACCIDENT VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ TRAVEL_SPEED_GROUP$ MODEL_YEAR_OF_VEHICLE TRAVEL_SPEED_GROUP$ AGE_OF_VEHICLE SEX$ YEAR_OF_ACCIDENT AGE_OF_VEHICLE VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ VEHICLE_WEIGHT_GROUP$ OCCUPANT_AGE_GROUPS$ AGE_OF_VEHICLE AGE_OF_VEHICLE YEAR_OF_ACCIDENT YEAR_OF_ACCIDENT MODEL_YEAR_OF_VEHICLE MODEL_YEAR_OF_VEHICLE OCCUPANT_AGE_GROUPS$ OCCUPANT_AGE_GROUPS$ AGE_OF_VEHICLE OCCUPANT_AGE_GROUPS$ OCCUPANT_AGE_GROUPS$ AGE_OF_VEHICLE CAR_COMPANY_HQ_REGION$ MODEL_YEAR_OF_VEHICLE YEAR_OF_ACCIDENT OCCUPANT_AGE_GROUPS$ VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ VEHICLE_WEIGHT_GROUP$ TRAVEL_SPEED_GROUP$ OCCUPANT_AGE_GROUPS$ EXTRICATED_Y_N$ SB_USED_Y_N$ CART TREE TOPOGRAPHY: TOP-LEVEL SPLITTER NAME VIEW
  • 42. 42 EXTRICATED_Y_N$ = (Yes) Terminal Node 1 Class = Yes Class Cases % No 6866 17.4 Yes 32540 82.6 W = 39406.000 N = 39406 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 2 Class = Yes Class Cases % No 3922 32.6 Yes 8113 67.4 W = 12035.000 N = 12035 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Terminal Node 3 Class = Yes Class Cases % No 33 28.2 Yes 84 71.8 W = 117.000 N = 117 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Terminal Node 4 Class = No Class Cases % No 189 63.2 Yes 110 36.8 W = 299.000 N = 299 TRAVEL_SPEED_GROUP$ = (0 to 15) Node 7 Class = Yes OCCUPANT_AGE_GROUPS$ = (0 to 11,75+) Class Cases % No 222 53.4 Yes 194 46.6 W = 416.000 N = 416 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 6 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 4144 33.3 Yes 8307 66.7 W = 12451.000 N = 12451 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 5 Class = Yes Class Cases % No 102 28.3 Yes 258 71.7 W = 360.000 N = 360 OCCUPANT_AGE_GROUPS$ = (65 to 74,...) Terminal Node 6 Class = Yes Class Cases % No 1118 39.8 Yes 1692 60.2 W = 2810.000 N = 2810 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Terminal Node 7 Class = Yes Class Cases % No 1685 48.6 Yes 1779 51.4 W = 3464.000 N = 3464 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 8 Class = Yes Class Cases % No 1 6.7 Yes 14 93.3 W = 15.000 N = 15 YEAR_OF_ACCIDENT <= 1984.50 Terminal Node 9 Class = No Class Cases % No 866 60.2 Yes 573 39.8 W = 1439.000 N = 1439 YEAR_OF_ACCIDENT > 1984.50 Terminal Node 10 Class = Yes Class Cases % No 309 52.8 Yes 276 47.2 W = 585.000 N = 585 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 13 Class = No YEAR_OF_ACCIDENT <= 1984.50 Class Cases % No 1175 58.1 Yes 849 41.9 W = 2024.000 N = 2024 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Node 12 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 1176 57.7 Yes 863 42.3 W = 2039.000 N = 2039 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 11 Class = Yes VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 2861 52.0 Yes 2642 48.0 W = 5503.000 N = 5503 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 10 Class = Yes OCCUPANT_AGE_GROUPS$ = (65 to 74,75+) Class Cases % No 3979 47.9 Yes 4334 52.1 W = 8313.000 N = 8313 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Terminal Node 11 Class = Yes Class Cases % No 73 43.2 Yes 96 56.8 W = 169.000 N = 169 YEAR_OF_ACCIDENT <= 1987.50 Terminal Node 12 Class = No Class Cases % No 452 72.2 Yes 174 27.8 W = 626.000 N = 626 OCCUPANT_AGE_GROUPS$ = (55 to 64,...) Terminal Node 13 Class = Yes Class Cases % No 32 38.1 Yes 52 61.9 W = 84.000 N = 84 OCCUPANT_AGE_GROUPS$ = (45 to 54) Terminal Node 14 Class = No Class Cases % No 43 71.7 Yes 17 28.3 W = 60.000 N = 60 YEAR_OF_ACCIDENT > 1987.50 Node 16 Class = Yes OCCUPANT_AGE_GROUPS$ = (55 to 64,65 to 74) Class Cases % No 75 52.1 Yes 69 47.9 W = 144.000 N = 144 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 15 Class = No YEAR_OF_ACCIDENT <= 1987.50 Class Cases % No 527 68.4 Yes 243 31.6 W = 770.000 N = 770 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 14 Class = No OCCUPANT_AGE_GROUPS$ = (0 to 11,75+) Class Cases % No 600 63.9 Yes 339 36.1 W = 939.000 N = 939 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 9 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 4579 49.5 Yes 4673 50.5 W = 9252.000 N = 9252 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 8 Class = Yes TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 4681 48.7 Yes 4931 51.3 W = 9612.000 N = 9612 YEAR_OF_ACCIDENT <= 1989.50 Node 5 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 8825 40.0 Yes 13238 60.0 W = 22063.000 N = 22063 YEAR_OF_ACCIDENT > 1989.50 Terminal Node 15 Class = Yes Class Cases % No 10251 23.1 Yes 34161 76.9 W = 44412.000 N = 44412 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 4 Class = Yes YEAR_OF_ACCIDENT <= 1989.50 Class Cases % No 19076 28.7 Yes 47399 71.3 W = 66475.000 N = 66475 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 16 Class = Yes Class Cases % No 9939 37.9 Yes 16262 62.1 W = 26201.000 N = 26201 CAR_COMPANY_HQ_REGION$ = (Europe,...) Terminal Node 17 Class = Yes Class Cases % No 8559 36.2 Yes 15094 63.8 W = 23653.000 N = 23653 VEHICLE_WEIGHT_GROUP$ = (< 2451) Terminal Node 18 Class = Yes Class Cases % No 3799 35.6 Yes 6862 64.4 W = 10661.000 N = 10661 OCCUPANT_AGE_GROUPS$ = (35 to 44) Terminal Node 19 Class = Yes Class Cases % No 4842 47.4 Yes 5377 52.6 W = 10219.000 N = 10219 YEAR_OF_ACCIDENT <= 1985.50 Terminal Node 20 Class = Yes Class Cases % No 1561 52.6 Yes 1409 47.4 W = 2970.000 N = 2970 YEAR_OF_ACCIDENT > 1985.50 Terminal Node 21 Class = No Class Cases % No 3905 57.4 Yes 2898 42.6 W = 6803.000 N = 6803 AGE_OF_VEHICLE <= 5.50 Node 26 Class = Yes YEAR_OF_ACCIDENT <= 1985.50 Class Cases % No 5466 55.9 Yes 4307 44.1 W = 9773.000 N = 9773 AGE_OF_VEHICLE > 5.50 Terminal Node 22 Class = Yes Class Cases % No 6862 52.9 Yes 6119 47.1 W = 12981.000 N = 12981 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 25 Class = Yes AGE_OF_VEHICLE <= 5.50 Class Cases % No 12328 54.2 Yes 10426 45.8 W = 22754.000 N = 22754 YEAR_OF_ACCIDENT <= 1998.50 Terminal Node 23 Class = No Class Cases % No 4746 59.3 Yes 3254 40.7 W = 8000.000 N = 8000 YEAR_OF_ACCIDENT > 1998.50 Terminal Node 24 Class = Yes Class Cases % No 767 55.9 Yes 604 44.1 W = 1371.000 N = 1371 MODEL_YEAR_OF_VEHICLE <= 1997.50 Node 28 Class = No YEAR_OF_ACCIDENT <= 1998.50 Class Cases % No 5513 58.8 Yes 3858 41.2 W = 9371.000 N = 9371 MODEL_YEAR_OF_VEHICLE > 1997.50 Terminal Node 25 Class = Yes Class Cases % No 151 47.9 Yes 164 52.1 W = 315.000 N = 315 OCCUPANT_AGE_GROUPS$ = (16 to 20) Node 27 Class = No MODEL_YEAR_OF_VEHICLE <= 1997.50 Class Cases % No 5664 58.5 Yes 4022 41.5 W = 9686.000 N = 9686 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 24 Class = Yes OCCUPANT_AGE_GROUPS$ = (12 to 15,21 to 24, 25 to 34) Class Cases % No 17992 55.5 Yes 14448 44.5 W = 32440.000 N = 32440 YEAR_OF_ACCIDENT <= 2003.50 Node 23 Class = Yes OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 22834 53.5 Yes 19825 46.5 W = 42659.000 N = 42659 YEAR_OF_ACCIDENT > 2003.50 Terminal Node 26 Class = Yes Class Cases % No 3453 42.1 Yes 4757 57.9 W = 8210.000 N = 8210 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 22 Class = Yes YEAR_OF_ACCIDENT <= 2003.50 Class Cases % No 26287 51.7 Yes 24582 48.3 W = 50869.000 N = 50869 CAR_COMPANY_HQ_REGION$ = (USA) Node 21 Class = Yes VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 30086 48.9 Yes 31444 51.1 W = 61530.000 N = 61530 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 20 Class = Yes CAR_COMPANY_HQ_REGION$ = (Europe,Japan,Korea, Other Imports) Class Cases % No 38645 45.4 Yes 46538 54.6 W = 85183.000 N = 85183 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 27 Class = Yes Class Cases % No 110 41.7 Yes 154 58.3 W = 264.000 N = 264 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 28 Class = No Class Cases % No 67 69.1 Yes 30 30.9 W = 97.000 N = 97 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 32 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35) Class Cases % No 177 49.0 Yes 184 51.0 W = 361.000 N = 361 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 29 Class = No Class Cases % No 689 64.2 Yes 385 35.8 W = 1074.000 N = 1074 YEAR_OF_ACCIDENT <= 2007.50 Node 31 Class = No OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 866 60.3 Yes 569 39.7 W = 1435.000 N = 1435 YEAR_OF_ACCIDENT > 2007.50 Terminal Node 30 Class = Yes Class Cases % No 89 40.6 Yes 130 59.4 W = 219.000 N = 219 CAR_COMPANY_HQ_REGION$ = (Europe,...) Node 30 Class = No YEAR_OF_ACCIDENT <= 2007.50 Class Cases % No 955 57.7 Yes 699 42.3 W = 1654.000 N = 1654 CAR_COMPANY_HQ_REGION$ = (Other Impo...) Terminal Node 31 Class = No Class Cases % No 3202 73.1 Yes 1180 26.9 W = 4382.000 N = 4382 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 29 Class = No CAR_COMPANY_HQ_REGION$ = (Europe,Japan,Korea) Class Cases % No 4157 68.9 Yes 1879 31.1 W = 6036.000 N = 6036 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 19 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 42802 46.9 Yes 48417 53.1 W = 91219.000 N = 91219 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 32 Class = Yes Class Cases % No 93 34.2 Yes 179 65.8 W = 272.000 N = 272 MODEL_YEAR_OF_VEHICLE <= 1977.50 Terminal Node 33 Class = No Class Cases % No 5613 66.4 Yes 2843 33.6 W = 8456.000 N = 8456 YEAR_OF_ACCIDENT <= 1985.50 Terminal Node 34 Class = No Class Cases % No 2365 62.8 Yes 1400 37.2 W = 3765.000 N = 3765 OCCUPANT_AGE_GROUPS$ = (21 to 24,...) Terminal Node 35 Class = Yes Class Cases % No 1586 53.3 Yes 1391 46.7 W = 2977.000 N = 2977 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 36 Class = No Class Cases % No 514 64.3 Yes 286 35.8 W = 800.000 N = 800 YEAR_OF_ACCIDENT > 1985.50 Node 39 Class = Yes OCCUPANT_AGE_GROUPS$ = (21 to 24,25 to 34, 35 to 44) Class Cases % No 2100 55.6 Yes 1677 44.4 W = 3777.000 N = 3777 MODEL_YEAR_OF_VEHICLE > 1977.50 Node 38 Class = No YEAR_OF_ACCIDENT <= 1985.50 Class Cases % No 4465 59.2 Yes 3077 40.8 W = 7542.000 N = 7542 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 37 Class = No MODEL_YEAR_OF_VEHICLE <= 1977.50 Class Cases % No 10078 63.0 Yes 5920 37.0 W = 15998.000 N = 15998 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 36 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 10171 62.5 Yes 6099 37.5 W = 16270.000 N = 16270 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 37 Class = No Class Cases % No 3680 72.7 Yes 1385 27.3 W = 5065.000 N = 5065 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 35 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 13851 64.9 Yes 7484 35.1 W = 21335.000 N = 21335 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 38 Class = No Class Cases % No 1328 84.6 Yes 241 15.4 W = 1569.000 N = 1569 YEAR_OF_ACCIDENT <= 1993.50 Node 34 Class = No TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 15179 66.3 Yes 7725 33.7 W = 22904.000 N = 22904 MODEL_YEAR_OF_VEHICLE <= 1976.50 Terminal Node 39 Class = No Class Cases % No 80 61.5 Yes 50 38.5 W = 130.000 N = 130 MODEL_YEAR_OF_VEHICLE > 1976.50 Terminal Node 40 Class = Yes Class Cases % No 947 37.5 Yes 1575 62.5 W = 2522.000 N = 2522 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 42 Class = Yes MODEL_YEAR_OF_VEHICLE <= 1976.50 Class Cases % No 1027 38.7 Yes 1625 61.3 W = 2652.000 N = 2652 MODEL_YEAR_OF_VEHICLE <= 1979.50 Terminal Node 41 Class = No Class Cases % No 357 64.8 Yes 194 35.2 W = 551.000 N = 551 YEAR_OF_ACCIDENT <= 1995.50 Terminal Node 42 Class = No Class Cases % No 321 59.3 Yes 220 40.7 W = 541.000 N = 541 YEAR_OF_ACCIDENT > 1995.50 Terminal Node 43 Class = Yes Class Cases % No 691 52.0 Yes 638 48.0 W = 1329.000 N = 1329 MODEL_YEAR_OF_VEHICLE > 1979.50 Node 46 Class = Yes YEAR_OF_ACCIDENT <= 1995.50 Class Cases % No 1012 54.1 Yes 858 45.9 W = 1870.000 N = 1870 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 45 Class = Yes MODEL_YEAR_OF_VEHICLE <= 1979.50 Class Cases % No 1369 56.5 Yes 1052 43.5 W = 2421.000 N = 2421 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 44 Class = No Class Cases % No 257 66.8 Yes 128 33.2 W = 385.000 N = 385 YEAR_OF_ACCIDENT <= 2000.50 Node 44 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 1626 57.9 Yes 1180 42.1 W = 2806.000 N = 2806 YEAR_OF_ACCIDENT > 2000.50 Terminal Node 45 Class = Yes Class Cases % No 1821 46.8 Yes 2067 53.2 W = 3888.000 N = 3888 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 43 Class = Yes YEAR_OF_ACCIDENT <= 2000.50 Class Cases % No 3447 51.5 Yes 3247 48.5 W = 6694.000 N = 6694 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 41 Class = Yes OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 4474 47.9 Yes 4872 52.1 W = 9346.000 N = 9346 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 46 Class = No Class Cases % No 438 73.4 Yes 159 26.6 W = 597.000 N = 597 YEAR_OF_ACCIDENT > 1993.50 Node 40 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 4912 49.4 Yes 5031 50.6 W = 9943.000 N = 9943 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 33 Class = No YEAR_OF_ACCIDENT <= 1993.50 Class Cases % No 20091 61.2 Yes 12756 38.8 W = 32847.000 N = 32847 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 18 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 62893 50.7 Yes 61173 49.3 W = 124066.000 N = 124066 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 17 Class = Yes TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 72832 48.5 Yes 77435 51.5 W = 150267.000 N = 150267 EXTRICATED_Y_N$ = (No) Node 3 Class = Yes OCCUPANT_AGE_GROUPS$ = (0 to 11,45 to 54, 55 to 64,65 to 74, 75+) Class Cases % No 91908 42.4 Yes 124834 57.6 W = 216742.000 N = 216742 SB_USED_Y_N$ = (No) Node 2 Class = Yes EXTRICATED_Y_N$ = (Yes) Class Cases % No 98774 38.6 Yes 157374 61.4 W = 256148.000 N = 256148 EXTRICATED_Y_N$ = (Yes) Terminal Node 47 Class = Yes Class Cases % No 13643 31.7 Yes 29455 68.3 W = 43098.000 N = 43098 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Terminal Node 48 Class = Yes Class Cases % No 3290 36.3 Yes 5770 63.7 W = 9060.000 N = 9060 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 49 Class = Yes Class Cases % No 4541 46.0 Yes 5334 54.0 W = 9875.000 N = 9875 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 50 Class = No Class Cases % No 474 63.9 Yes 268 36.1 W = 742.000 N = 742 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 51 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 5015 47.2 Yes 5602 52.8 W = 10617.000 N = 10617 OCCUPANT_AGE_GROUPS$ = (75+) Node 50 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 8305 42.2 Yes 11372 57.8 W = 19677.000 N = 19677 VEHICLE_WEIGHT_GROUP$ = (< 2451) Terminal Node 51 Class = Yes Class Cases % No 612 38.3 Yes 987 61.7 W = 1599.000 N = 1599 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Terminal Node 52 Class = Yes Class Cases % No 857 50.8 Yes 831 49.2 W = 1688.000 N = 1688 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 53 Class = No Class Cases % No 108 64.3 Yes 60 35.7 W = 168.000 N = 168 SEX$ = (Female) Node 57 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 965 52.0 Yes 891 48.0 W = 1856.000 N = 1856 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 54 Class = Yes Class Cases % No 127 53.4 Yes 111 46.6 W = 238.000 N = 238 AGE_OF_VEHICLE <= 6.50 Terminal Node 55 Class = No Class Cases % No 151 61.9 Yes 93 38.1 W = 244.000 N = 244 AGE_OF_VEHICLE > 6.50 Terminal Node 56 Class = Yes Class Cases % No 104 48.6 Yes 110 51.4 W = 214.000 N = 214 CAR_COMPANY_HQ_REGION$ = (Japan,...) Node 60 Class = Yes AGE_OF_VEHICLE <= 6.50 Class Cases % No 255 55.7 Yes 203 44.3 W = 458.000 N = 458 CAR_COMPANY_HQ_REGION$ = (Europe,...) Terminal Node 57 Class = No Class Cases % No 1818 64.9 Yes 985 35.1 W = 2803.000 N = 2803 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 59 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Other Imports) Class Cases % No 2073 63.6 Yes 1188 36.4 W = 3261.000 N = 3261 SEX$ = (*,Male) Node 58 Class = No TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 2200 62.9 Yes 1299 37.1 W = 3499.000 N = 3499 YEAR_OF_ACCIDENT <= 2003.50 Node 56 Class = No SEX$ = (Female) Class Cases % No 3165 59.1 Yes 2190 40.9 W = 5355.000 N = 5355 YEAR_OF_ACCIDENT > 2003.50 Terminal Node 58 Class = Yes Class Cases % No 825 47.1 Yes 925 52.9 W = 1750.000 N = 1750 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 55 Class = Yes YEAR_OF_ACCIDENT <= 2003.50 Class Cases % No 3990 56.2 Yes 3115 43.8 W = 7105.000 N = 7105 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 54 Class = Yes VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 4602 52.9 Yes 4102 47.1 W = 8704.000 N = 8704 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 59 Class = No Class Cases % No 407 76.5 Yes 125 23.5 W = 532.000 N = 532 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 53 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 5009 54.2 Yes 4227 45.8 W = 9236.000 N = 9236 TRAVEL_SPEED_GROUP$ = (> 75) Terminal Node 60 Class = Yes Class Cases % No 23 41.8 Yes 32 58.2 W = 55.000 N = 55 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 61 Class = No Class Cases % No 4383 68.0 Yes 2060 32.0 W = 6443.000 N = 6443 AGE_OF_VEHICLE <= 8.50 Node 64 Class = No TRAVEL_SPEED_GROUP$ = (> 75) Class Cases % No 4406 67.8 Yes 2092 32.2 W = 6498.000 N = 6498 YEAR_OF_ACCIDENT <= 1992.50 Terminal Node 62 Class = No Class Cases % No 166 72.2 Yes 64 27.8 W = 230.000 N = 230 MODEL_YEAR_OF_VEHICLE <= 2001.50 Terminal Node 63 Class = Yes Class Cases % No 313 47.9 Yes 340 52.1 W = 653.000 N = 653 MODEL_YEAR_OF_VEHICLE > 2001.50 Terminal Node 64 Class = No Class Cases % No 272 58.6 Yes 192 41.4 W = 464.000 N = 464 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Node 68 Class = Yes MODEL_YEAR_OF_VEHICLE <= 2001.50 Class Cases % No 585 52.4 Yes 532 47.6 W = 1117.000 N = 1117 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 65 Class = No Class Cases % No 73 62.9 Yes 43 37.1 W = 116.000 N = 116 SEX$ = (*,Female) Node 67 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 658 53.4 Yes 575 46.6 W = 1233.000 N = 1233 AGE_OF_VEHICLE <= 14.50 Terminal Node 66 Class = No Class Cases % No 1068 61.2 Yes 677 38.8 W = 1745.000 N = 1745 AGE_OF_VEHICLE > 14.50 Terminal Node 67 Class = Yes Class Cases % No 336 54.8 Yes 277 45.2 W = 613.000 N = 613 SEX$ = (Male) Node 69 Class = No AGE_OF_VEHICLE <= 14.50 Class Cases % No 1404 59.5 Yes 954 40.5 W = 2358.000 N = 2358 YEAR_OF_ACCIDENT > 1992.50 Node 66 Class = No SEX$ = (*,Female) Class Cases % No 2062 57.4 Yes 1529 42.6 W = 3591.000 N = 3591 AGE_OF_VEHICLE > 8.50 Node 65 Class = No YEAR_OF_ACCIDENT <= 1992.50 Class Cases % No 2228 58.3 Yes 1593 41.7 W = 3821.000 N = 3821 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 63 Class = No AGE_OF_VEHICLE <= 8.50 Class Cases % No 6634 64.3 Yes 3685 35.7 W = 10319.000 N = 10319 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 68 Class = No Class Cases % No 1237 72.7 Yes 465 27.3 W = 1702.000 N = 1702 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 62 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 7871 65.5 Yes 4150 34.5 W = 12021.000 N = 12021 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 69 Class = No Class Cases % No 619 84.8 Yes 111 15.2 W = 730.000 N = 730 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 61 Class = No TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 8490 66.6 Yes 4261 33.4 W = 12751.000 N = 12751 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 52 Class = No VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 13499 61.4 Yes 8488 38.6 W = 21987.000 N = 21987 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 49 Class = Yes OCCUPANT_AGE_GROUPS$ = (75+) Class Cases % No 21804 52.3 Yes 19860 47.7 W = 41664.000 N = 41664 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 70 Class = No Class Cases % No 9284 69.0 Yes 4172 31.0 W = 13456.000 N = 13456 YEAR_OF_ACCIDENT <= 1980.50 Terminal Node 71 Class = No Class Cases % No 43 70.5 Yes 18 29.5 W = 61.000 N = 61 AGE_OF_VEHICLE <= 5.50 Terminal Node 72 Class = No Class Cases % No 368 58.0 Yes 266 42.0 W = 634.000 N = 634 AGE_OF_VEHICLE > 5.50 Terminal Node 73 Class = Yes Class Cases % No 38 38.4 Yes 61 61.6 W = 99.000 N = 99 AGE_OF_VEHICLE <= 6.50 Node 78 Class = Yes AGE_OF_VEHICLE <= 5.50 Class Cases % No 406 55.4 Yes 327 44.6 W = 733.000 N = 733 AGE_OF_VEHICLE > 6.50 Terminal Node 74 Class = No Class Cases % No 162 63.0 Yes 95 37.0 W = 257.000 N = 257 YEAR_OF_ACCIDENT > 1980.50 Node 77 Class = No AGE_OF_VEHICLE <= 6.50 Class Cases % No 568 57.4 Yes 422 42.6 W = 990.000 N = 990 YEAR_OF_ACCIDENT <= 1990.50 Node 76 Class = No YEAR_OF_ACCIDENT <= 1980.50 Class Cases % No 611 58.1 Yes 440 41.9 W = 1051.000 N = 1051 YEAR_OF_ACCIDENT > 1990.50 Terminal Node 75 Class = Yes Class Cases % No 1386 46.7 Yes 1584 53.3 W = 2970.000 N = 2970 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 75 Class = Yes YEAR_OF_ACCIDENT <= 1990.50 Class Cases % No 1997 49.7 Yes 2024 50.3 W = 4021.000 N = 4021 MODEL_YEAR_OF_VEHICLE <= 1988.50 Terminal Node 76 Class = No Class Cases % No 954 63.5 Yes 548 36.5 W = 1502.000 N = 1502 MODEL_YEAR_OF_VEHICLE <= 1994.50 Terminal Node 77 Class = Yes Class Cases % No 485 53.7 Yes 419 46.3 W = 904.000 N = 904 MODEL_YEAR_OF_VEHICLE > 1994.50 Terminal Node 78 Class = No Class Cases % No 331 64.6 Yes 181 35.4 W = 512.000 N = 512 MODEL_YEAR_OF_VEHICLE > 1988.50 Node 82 Class = No MODEL_YEAR_OF_VEHICLE <= 1994.50 Class Cases % No 816 57.6 Yes 600 42.4 W = 1416.000 N = 1416 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 81 Class = No MODEL_YEAR_OF_VEHICLE <= 1988.50 Class Cases % No 1770 60.7 Yes 1148 39.3 W = 2918.000 N = 2918 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 79 Class = No Class Cases % No 7443 67.4 Yes 3598 32.6 W = 11041.000 N = 11041 AGE_OF_VEHICLE <= 13.50 Node 80 Class = No OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 9213 66.0 Yes 4746 34.0 W = 13959.000 N = 13959 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 80 Class = Yes Class Cases % No 584 52.1 Yes 536 47.9 W = 1120.000 N = 1120 OCCUPANT_AGE_GROUPS$ = (16 to 20) Terminal Node 81 Class = No Class Cases % No 246 62.8 Yes 146 37.2 W = 392.000 N = 392 AGE_OF_VEHICLE > 13.50 Node 83 Class = Yes OCCUPANT_AGE_GROUPS$ = (12 to 15,21 to 24, 25 to 34,35 to 44) Class Cases % No 830 54.9 Yes 682 45.1 W = 1512.000 N = 1512 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 79 Class = No AGE_OF_VEHICLE <= 13.50 Class Cases % No 10043 64.9 Yes 5428 35.1 W = 15471.000 N = 15471 VEHICLE_WEIGHT_GROUP$ = (< 2451) Node 74 Class = No OCCUPANT_AGE_GROUPS$ = (45 to 54,55 to 64) Class Cases % No 12040 61.8 Yes 7452 38.2 W = 19492.000 N = 19492 YEAR_OF_ACCIDENT <= 2004.50 Terminal Node 82 Class = No Class Cases % No 13382 75.5 Yes 4347 24.5 W = 17729.000 N = 17729 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 83 Class = Yes Class Cases % No 139 37.0 Yes 237 63.0 W = 376.000 N = 376 AGE_OF_VEHICLE <= 16.50 Terminal Node 84 Class = No Class Cases % No 148 69.5 Yes 65 30.5 W = 213.000 N = 213 OCCUPANT_AGE_GROUPS$ = (55 to 64) Terminal Node 85 Class = Yes Class Cases % No 180 52.8 Yes 161 47.2 W = 341.000 N = 341 OCCUPANT_AGE_GROUPS$ = (45 to 54) Terminal Node 86 Class = No Class Cases % No 298 65.5 Yes 157 34.5 W = 455.000 N = 455 AGE_OF_VEHICLE > 16.50 Node 89 Class = No OCCUPANT_AGE_GROUPS$ = (55 to 64) Class Cases % No 478 60.1 Yes 318 39.9 W = 796.000 N = 796 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 88 Class = No AGE_OF_VEHICLE <= 16.50 Class Cases % No 626 62.0 Yes 383 38.0 W = 1009.000 N = 1009 MODEL_YEAR_OF_VEHICLE <= 1992.50 Node 87 Class = Yes CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 765 55.2 Yes 620 44.8 W = 1385.000 N = 1385 MODEL_YEAR_OF_VEHICLE > 1992.50 Terminal Node 87 Class = No Class Cases % No 3023 66.9 Yes 1498 33.1 W = 4521.000 N = 4521 YEAR_OF_ACCIDENT > 2004.50 Node 86 Class = No MODEL_YEAR_OF_VEHICLE <= 1992.50 Class Cases % No 3788 64.1 Yes 2118 35.9 W = 5906.000 N = 5906 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 85 Class = No YEAR_OF_ACCIDENT <= 2004.50 Class Cases % No 17170 72.6 Yes 6465 27.4 W = 23635.000 N = 23635 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 88 Class = No Class Cases % No 54887 81.6 Yes 12350 18.4 W = 67237.000 N = 67237 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 84 Class = No OCCUPANT_AGE_GROUPS$ = (45 to 54,55 to 64) Class Cases % No 72057 79.3 Yes 18815 20.7 W = 90872.000 N = 90872 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 73 Class = No VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 84097 76.2 Yes 26267 23.8 W = 110364.000 N = 110364 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 72 Class = No TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 93381 75.4 Yes 30439 24.6 W = 123820.000 N = 123820 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Terminal Node 89 Class = No Class Cases % No 86853 82.6 Yes 18285 17.4 W = 105138.000 N = 105138 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Node 71 Class = No VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 180234 78.7 Yes 48724 21.3 W = 228958.000 N = 228958 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 90 Class = No Class Cases % No 23988 92.8 Yes 1866 7.2 W = 25854.000 N = 25854 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 70 Class = No TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 204222 80.1 Yes 50590 19.9 W = 254812.000 N = 254812 EXTRICATED_Y_N$ = (No) Node 48 Class = No OCCUPANT_AGE_GROUPS$ = (0 to 11,65 to 74, 75+) Class Cases % No 226026 76.2 Yes 70450 23.8 W = 296476.000 N = 296476 SB_USED_Y_N$ = (Yes) Node 47 Class = No EXTRICATED_Y_N$ = (Yes) Class Cases % No 239669 70.6 Yes 99905 29.4 W = 339574.000 N = 339574 Node 1 Class = Yes SB_USED_Y_N$ = (No) Class Cases % No 338443 56.8 Yes 257279 43.2 W = 595722.000 N = 595722 SB wornNo SB Worn Lower Fatalities Higher Fatalities CART TREE TOPOGRAPHY: DETAILED NODE DATA BOX VIEW
  • 43. 43 EXTRICATED_Y_N$ = (Yes) Terminal Node 1 Class = Yes Class Cases % No 6866 17.4 Yes 32540 82.6 W = 39406.000 N = 39406 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 2 Class = Yes Class Cases % No 3922 32.6 Yes 8113 67.4 W = 12035.000 N = 12035 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Terminal Node 3 Class = Yes Class Cases % No 33 28.2 Yes 84 71.8 W = 117.000 N = 117 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Terminal Node 4 Class = No Class Cases % No 189 63.2 Yes 110 36.8 W = 299.000 N = 299 TRAVEL_SPEED_GROUP$ = (0 to 15) Node 7 Class = Yes OCCUPANT_AGE_GROUPS$ = (0 to 11,75+) Class Cases % No 222 53.4 Yes 194 46.6 W = 416.000 N = 416 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 6 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 4144 33.3 Yes 8307 66.7 W = 12451.000 N = 12451 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 5 Class = Yes Class Cases % No 102 28.3 Yes 258 71.7 W = 360.000 N = 360 OCCUPANT_AGE_GROUPS$ = (65 to 74,...) Terminal Node 6 Class = Yes Class Cases % No 1118 39.8 Yes 1692 60.2 W = 2810.000 N = 2810 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Terminal Node 7 Class = Yes Class Cases % No 1685 48.6 Yes 1779 51.4 W = 3464.000 N = 3464 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 8 Class = Yes Class Cases % No 1 6.7 Yes 14 93.3 W = 15.000 N = 15 YEAR_OF_ACCIDENT <= 1984.50 Terminal Node 9 Class = No Class Cases % No 866 60.2 Yes 573 39.8 W = 1439.000 N = 1439 YEAR_OF_ACCIDENT > 1984.50 Terminal Node 10 Class = Yes Class Cases % No 309 52.8 Yes 276 47.2 W = 585.000 N = 585 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 13 Class = No YEAR_OF_ACCIDENT <= 1984.50 Class Cases % No 1175 58.1 Yes 849 41.9 W = 2024.000 N = 2024 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Node 12 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 1176 57.7 Yes 863 42.3 W = 2039.000 N = 2039 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 11 Class = Yes VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 2861 52.0 Yes 2642 48.0 W = 5503.000 N = 5503 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 10 Class = Yes OCCUPANT_AGE_GROUPS$ = (65 to 74,75+) Class Cases % No 3979 47.9 Yes 4334 52.1 W = 8313.000 N = 8313 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Terminal Node 11 Class = Yes Class Cases % No 73 43.2 Yes 96 56.8 W = 169.000 N = 169 YEAR_OF_ACCIDENT <= 1987.50 Terminal Node 12 Class = No Class Cases % No 452 72.2 Yes 174 27.8 W = 626.000 N = 626 OCCUPANT_AGE_GROUPS$ = (55 to 64,...) Terminal Node 13 Class = Yes Class Cases % No 32 38.1 Yes 52 61.9 W = 84.000 N = 84 OCCUPANT_AGE_GROUPS$ = (45 to 54) Terminal Node 14 Class = No Class Cases % No 43 71.7 Yes 17 28.3 W = 60.000 N = 60 YEAR_OF_ACCIDENT > 1987.50 Node 16 Class = Yes OCCUPANT_AGE_GROUPS$ = (55 to 64,65 to 74) Class Cases % No 75 52.1 Yes 69 47.9 W = 144.000 N = 144 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 15 Class = No YEAR_OF_ACCIDENT <= 1987.50 Class Cases % No 527 68.4 Yes 243 31.6 W = 770.000 N = 770 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 14 Class = No OCCUPANT_AGE_GROUPS$ = (0 to 11,75+) Class Cases % No 600 63.9 Yes 339 36.1 W = 939.000 N = 939 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 9 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 4579 49.5 Yes 4673 50.5 W = 9252.000 N = 9252 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 8 Class = Yes TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 4681 48.7 Yes 4931 51.3 W = 9612.000 N = 9612 YEAR_OF_ACCIDENT <= 1989.50 Node 5 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 8825 40.0 Yes 13238 60.0 W = 22063.000 N = 22063 YEAR_OF_ACCIDENT > 1989.50 Terminal Node 15 Class = Yes Class Cases % No 10251 23.1 Yes 34161 76.9 W = 44412.000 N = 44412 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 4 Class = Yes YEAR_OF_ACCIDENT <= 1989.50 Class Cases % No 19076 28.7 Yes 47399 71.3 W = 66475.000 N = 66475 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 16 Class = Yes Class Cases % No 9939 37.9 Yes 16262 62.1 W = 26201.000 N = 26201 CAR_COMPANY_HQ_REGION$ = (Europe,...) Terminal Node 17 Class = Yes Class Cases % No 8559 36.2 Yes 15094 63.8 W = 23653.000 N = 23653 VEHICLE_WEIGHT_GROUP$ = (< 2451) Terminal Node 18 Class = Yes Class Cases % No 3799 35.6 Yes 6862 64.4 W = 10661.000 N = 10661 OCCUPANT_AGE_GROUPS$ = (35 to 44) Terminal Node 19 Class = Yes Class Cases % No 4842 47.4 Yes 5377 52.6 W = 10219.000 N = 10219 YEAR_OF_ACCIDENT <= 1985.50 Terminal Node 20 Class = Yes Class Cases % No 1561 52.6 Yes 1409 47.4 W = 2970.000 N = 2970 YEAR_OF_ACCIDENT > 1985.50 Terminal Node 21 Class = No Class Cases % No 3905 57.4 Yes 2898 42.6 W = 6803.000 N = 6803 AGE_OF_VEHICLE <= 5.50 Node 26 Class = Yes YEAR_OF_ACCIDENT <= 1985.50 Class Cases % No 5466 55.9 Yes 4307 44.1 W = 9773.000 N = 9773 AGE_OF_VEHICLE > 5.50 Terminal Node 22 Class = Yes Class Cases % No 6862 52.9 Yes 6119 47.1 W = 12981.000 N = 12981 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 25 Class = Yes AGE_OF_VEHICLE <= 5.50 Class Cases % No 12328 54.2 Yes 10426 45.8 W = 22754.000 N = 22754 YEAR_OF_ACCIDENT <= 1998.50 Terminal Node 23 Class = No Class Cases % No 4746 59.3 Yes 3254 40.7 W = 8000.000 N = 8000 YEAR_OF_ACCIDENT > 1998.50 Terminal Node 24 Class = Yes Class Cases % No 767 55.9 Yes 604 44.1 W = 1371.000 N = 1371 MODEL_YEAR_OF_VEHICLE <= 1997.50 Node 28 Class = No YEAR_OF_ACCIDENT <= 1998.50 Class Cases % No 5513 58.8 Yes 3858 41.2 W = 9371.000 N = 9371 MODEL_YEAR_OF_VEHICLE > 1997.50 Terminal Node 25 Class = Yes Class Cases % No 151 47.9 Yes 164 52.1 W = 315.000 N = 315 OCCUPANT_AGE_GROUPS$ = (16 to 20) Node 27 Class = No MODEL_YEAR_OF_VEHICLE <= 1997.50 Class Cases % No 5664 58.5 Yes 4022 41.5 W = 9686.000 N = 9686 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 24 Class = Yes OCCUPANT_AGE_GROUPS$ = (12 to 15,21 to 24, 25 to 34) Class Cases % No 17992 55.5 Yes 14448 44.5 W = 32440.000 N = 32440 YEAR_OF_ACCIDENT <= 2003.50 Node 23 Class = Yes OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 22834 53.5 Yes 19825 46.5 W = 42659.000 N = 42659 YEAR_OF_ACCIDENT > 2003.50 Terminal Node 26 Class = Yes Class Cases % No 3453 42.1 Yes 4757 57.9 W = 8210.000 N = 8210 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 22 Class = Yes YEAR_OF_ACCIDENT <= 2003.50 Class Cases % No 26287 51.7 Yes 24582 48.3 W = 50869.000 N = 50869 CAR_COMPANY_HQ_REGION$ = (USA) Node 21 Class = Yes VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 30086 48.9 Yes 31444 51.1 W = 61530.000 N = 61530 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 20 Class = Yes CAR_COMPANY_HQ_REGION$ = (Europe,Japan,Korea, Other Imports) Class Cases % No 38645 45.4 Yes 46538 54.6 W = 85183.000 N = 85183 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 27 Class = Yes Class Cases % No 110 41.7 Yes 154 58.3 W = 264.000 N = 264 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 28 Class = No Class Cases % No 67 69.1 Yes 30 30.9 W = 97.000 N = 97 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 32 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35) Class Cases % No 177 49.0 Yes 184 51.0 W = 361.000 N = 361 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 29 Class = No Class Cases % No 689 64.2 Yes 385 35.8 W = 1074.000 N = 1074 YEAR_OF_ACCIDENT <= 2007.50 Node 31 Class = No OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 866 60.3 Yes 569 39.7 W = 1435.000 N = 1435 YEAR_OF_ACCIDENT > 2007.50 Terminal Node 30 Class = Yes Class Cases % No 89 40.6 Yes 130 59.4 W = 219.000 N = 219 CAR_COMPANY_HQ_REGION$ = (Europe,...) Node 30 Class = No YEAR_OF_ACCIDENT <= 2007.50 Class Cases % No 955 57.7 Yes 699 42.3 W = 1654.000 N = 1654 CAR_COMPANY_HQ_REGION$ = (Other Impo...) Terminal Node 31 Class = No Class Cases % No 3202 73.1 Yes 1180 26.9 W = 4382.000 N = 4382 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 29 Class = No CAR_COMPANY_HQ_REGION$ = (Europe,Japan,Korea) Class Cases % No 4157 68.9 Yes 1879 31.1 W = 6036.000 N = 6036 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 19 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 42802 46.9 Yes 48417 53.1 W = 91219.000 N = 91219 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 32 Class = Yes Class Cases % No 93 34.2 Yes 179 65.8 W = 272.000 N = 272 MODEL_YEAR_OF_VEHICLE <= 1977.50 Terminal Node 33 Class = No Class Cases % No 5613 66.4 Yes 2843 33.6 W = 8456.000 N = 8456 YEAR_OF_ACCIDENT <= 1985.50 Terminal Node 34 Class = No Class Cases % No 2365 62.8 Yes 1400 37.2 W = 3765.000 N = 3765 OCCUPANT_AGE_GROUPS$ = (21 to 24,...) Terminal Node 35 Class = Yes Class Cases % No 1586 53.3 Yes 1391 46.7 W = 2977.000 N = 2977 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 36 Class = No Class Cases % No 514 64.3 Yes 286 35.8 W = 800.000 N = 800 YEAR_OF_ACCIDENT > 1985.50 Node 39 Class = Yes OCCUPANT_AGE_GROUPS$ = (21 to 24,25 to 34, 35 to 44) Class Cases % No 2100 55.6 Yes 1677 44.4 W = 3777.000 N = 3777 MODEL_YEAR_OF_VEHICLE > 1977.50 Node 38 Class = No YEAR_OF_ACCIDENT <= 1985.50 Class Cases % No 4465 59.2 Yes 3077 40.8 W = 7542.000 N = 7542 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 37 Class = No MODEL_YEAR_OF_VEHICLE <= 1977.50 Class Cases % No 10078 63.0 Yes 5920 37.0 W = 15998.000 N = 15998 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 36 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 10171 62.5 Yes 6099 37.5 W = 16270.000 N = 16270 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 37 Class = No Class Cases % No 3680 72.7 Yes 1385 27.3 W = 5065.000 N = 5065 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 35 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 13851 64.9 Yes 7484 35.1 W = 21335.000 N = 21335 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 38 Class = No Class Cases % No 1328 84.6 Yes 241 15.4 W = 1569.000 N = 1569 YEAR_OF_ACCIDENT <= 1993.50 Node 34 Class = No TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 15179 66.3 Yes 7725 33.7 W = 22904.000 N = 22904 MODEL_YEAR_OF_VEHICLE <= 1976.50 Terminal Node 39 Class = No Class Cases % No 80 61.5 Yes 50 38.5 W = 130.000 N = 130 MODEL_YEAR_OF_VEHICLE > 1976.50 Terminal Node 40 Class = Yes Class Cases % No 947 37.5 Yes 1575 62.5 W = 2522.000 N = 2522 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 42 Class = Yes MODEL_YEAR_OF_VEHICLE <= 1976.50 Class Cases % No 1027 38.7 Yes 1625 61.3 W = 2652.000 N = 2652 MODEL_YEAR_OF_VEHICLE <= 1979.50 Terminal Node 41 Class = No Class Cases % No 357 64.8 Yes 194 35.2 W = 551.000 N = 551 YEAR_OF_ACCIDENT <= 1995.50 Terminal Node 42 Class = No Class Cases % No 321 59.3 Yes 220 40.7 W = 541.000 N = 541 YEAR_OF_ACCIDENT > 1995.50 Terminal Node 43 Class = Yes Class Cases % No 691 52.0 Yes 638 48.0 W = 1329.000 N = 1329 MODEL_YEAR_OF_VEHICLE > 1979.50 Node 46 Class = Yes YEAR_OF_ACCIDENT <= 1995.50 Class Cases % No 1012 54.1 Yes 858 45.9 W = 1870.000 N = 1870 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 45 Class = Yes MODEL_YEAR_OF_VEHICLE <= 1979.50 Class Cases % No 1369 56.5 Yes 1052 43.5 W = 2421.000 N = 2421 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 44 Class = No Class Cases % No 257 66.8 Yes 128 33.2 W = 385.000 N = 385 YEAR_OF_ACCIDENT <= 2000.50 Node 44 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 1626 57.9 Yes 1180 42.1 W = 2806.000 N = 2806 YEAR_OF_ACCIDENT > 2000.50 Terminal Node 45 Class = Yes Class Cases % No 1821 46.8 Yes 2067 53.2 W = 3888.000 N = 3888 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 43 Class = Yes YEAR_OF_ACCIDENT <= 2000.50 Class Cases % No 3447 51.5 Yes 3247 48.5 W = 6694.000 N = 6694 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 41 Class = Yes OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 4474 47.9 Yes 4872 52.1 W = 9346.000 N = 9346 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 46 Class = No Class Cases % No 438 73.4 Yes 159 26.6 W = 597.000 N = 597 YEAR_OF_ACCIDENT > 1993.50 Node 40 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 4912 49.4 Yes 5031 50.6 W = 9943.000 N = 9943 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 33 Class = No YEAR_OF_ACCIDENT <= 1993.50 Class Cases % No 20091 61.2 Yes 12756 38.8 W = 32847.000 N = 32847 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 18 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 62893 50.7 Yes 61173 49.3 W = 124066.000 N = 124066 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 17 Class = Yes TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 72832 48.5 Yes 77435 51.5 W = 150267.000 N = 150267 EXTRICATED_Y_N$ = (No) Node 3 Class = Yes OCCUPANT_AGE_GROUPS$ = (0 to 11,45 to 54, 55 to 64,65 to 74, 75+) Class Cases % No 91908 42.4 Yes 124834 57.6 W = 216742.000 N = 216742 SB_USED_Y_N$ = (No) Node 2 Class = Yes EXTRICATED_Y_N$ = (Yes) Class Cases % No 98774 38.6 Yes 157374 61.4 W = 256148.000 N = 256148 EXTRICATED_Y_N$ = (Yes) Terminal Node 47 Class = Yes Class Cases % No 13643 31.7 Yes 29455 68.3 W = 43098.000 N = 43098 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Terminal Node 48 Class = Yes Class Cases % No 3290 36.3 Yes 5770 63.7 W = 9060.000 N = 9060 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 49 Class = Yes Class Cases % No 4541 46.0 Yes 5334 54.0 W = 9875.000 N = 9875 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 50 Class = No Class Cases % No 474 63.9 Yes 268 36.1 W = 742.000 N = 742 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 51 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 5015 47.2 Yes 5602 52.8 W = 10617.000 N = 10617 OCCUPANT_AGE_GROUPS$ = (75+) Node 50 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 8305 42.2 Yes 11372 57.8 W = 19677.000 N = 19677 VEHICLE_WEIGHT_GROUP$ = (< 2451) Terminal Node 51 Class = Yes Class Cases % No 612 38.3 Yes 987 61.7 W = 1599.000 N = 1599 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Terminal Node 52 Class = Yes Class Cases % No 857 50.8 Yes 831 49.2 W = 1688.000 N = 1688 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 53 Class = No Class Cases % No 108 64.3 Yes 60 35.7 W = 168.000 N = 168 SEX$ = (Female) Node 57 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 965 52.0 Yes 891 48.0 W = 1856.000 N = 1856 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 54 Class = Yes Class Cases % No 127 53.4 Yes 111 46.6 W = 238.000 N = 238 AGE_OF_VEHICLE <= 6.50 Terminal Node 55 Class = No Class Cases % No 151 61.9 Yes 93 38.1 W = 244.000 N = 244 AGE_OF_VEHICLE > 6.50 Terminal Node 56 Class = Yes Class Cases % No 104 48.6 Yes 110 51.4 W = 214.000 N = 214 CAR_COMPANY_HQ_REGION$ = (Japan,...) Node 60 Class = Yes AGE_OF_VEHICLE <= 6.50 Class Cases % No 255 55.7 Yes 203 44.3 W = 458.000 N = 458 CAR_COMPANY_HQ_REGION$ = (Europe,...) Terminal Node 57 Class = No Class Cases % No 1818 64.9 Yes 985 35.1 W = 2803.000 N = 2803 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 59 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Other Imports) Class Cases % No 2073 63.6 Yes 1188 36.4 W = 3261.000 N = 3261 SEX$ = (*,Male) Node 58 Class = No TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 2200 62.9 Yes 1299 37.1 W = 3499.000 N = 3499 YEAR_OF_ACCIDENT <= 2003.50 Node 56 Class = No SEX$ = (Female) Class Cases % No 3165 59.1 Yes 2190 40.9 W = 5355.000 N = 5355 YEAR_OF_ACCIDENT > 2003.50 Terminal Node 58 Class = Yes Class Cases % No 825 47.1 Yes 925 52.9 W = 1750.000 N = 1750 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 55 Class = Yes YEAR_OF_ACCIDENT <= 2003.50 Class Cases % No 3990 56.2 Yes 3115 43.8 W = 7105.000 N = 7105 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 54 Class = Yes VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 4602 52.9 Yes 4102 47.1 W = 8704.000 N = 8704 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 59 Class = No Class Cases % No 407 76.5 Yes 125 23.5 W = 532.000 N = 532 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 53 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 5009 54.2 Yes 4227 45.8 W = 9236.000 N = 9236 TRAVEL_SPEED_GROUP$ = (> 75) Terminal Node 60 Class = Yes Class Cases % No 23 41.8 Yes 32 58.2 W = 55.000 N = 55 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 61 Class = No Class Cases % No 4383 68.0 Yes 2060 32.0 W = 6443.000 N = 6443 AGE_OF_VEHICLE <= 8.50 Node 64 Class = No TRAVEL_SPEED_GROUP$ = (> 75) Class Cases % No 4406 67.8 Yes 2092 32.2 W = 6498.000 N = 6498 YEAR_OF_ACCIDENT <= 1992.50 Terminal Node 62 Class = No Class Cases % No 166 72.2 Yes 64 27.8 W = 230.000 N = 230 MODEL_YEAR_OF_VEHICLE <= 2001.50 Terminal Node 63 Class = Yes Class Cases % No 313 47.9 Yes 340 52.1 W = 653.000 N = 653 MODEL_YEAR_OF_VEHICLE > 2001.50 Terminal Node 64 Class = No Class Cases % No 272 58.6 Yes 192 41.4 W = 464.000 N = 464 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Node 68 Class = Yes MODEL_YEAR_OF_VEHICLE <= 2001.50 Class Cases % No 585 52.4 Yes 532 47.6 W = 1117.000 N = 1117 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 65 Class = No Class Cases % No 73 62.9 Yes 43 37.1 W = 116.000 N = 116 SEX$ = (*,Female) Node 67 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 658 53.4 Yes 575 46.6 W = 1233.000 N = 1233 AGE_OF_VEHICLE <= 14.50 Terminal Node 66 Class = No Class Cases % No 1068 61.2 Yes 677 38.8 W = 1745.000 N = 1745 AGE_OF_VEHICLE > 14.50 Terminal Node 67 Class = Yes Class Cases % No 336 54.8 Yes 277 45.2 W = 613.000 N = 613 SEX$ = (Male) Node 69 Class = No AGE_OF_VEHICLE <= 14.50 Class Cases % No 1404 59.5 Yes 954 40.5 W = 2358.000 N = 2358 YEAR_OF_ACCIDENT > 1992.50 Node 66 Class = No SEX$ = (*,Female) Class Cases % No 2062 57.4 Yes 1529 42.6 W = 3591.000 N = 3591 AGE_OF_VEHICLE > 8.50 Node 65 Class = No YEAR_OF_ACCIDENT <= 1992.50 Class Cases % No 2228 58.3 Yes 1593 41.7 W = 3821.000 N = 3821 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 63 Class = No AGE_OF_VEHICLE <= 8.50 Class Cases % No 6634 64.3 Yes 3685 35.7 W = 10319.000 N = 10319 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 68 Class = No Class Cases % No 1237 72.7 Yes 465 27.3 W = 1702.000 N = 1702 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 62 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 7871 65.5 Yes 4150 34.5 W = 12021.000 N = 12021 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 69 Class = No Class Cases % No 619 84.8 Yes 111 15.2 W = 730.000 N = 730 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 61 Class = No TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 8490 66.6 Yes 4261 33.4 W = 12751.000 N = 12751 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 52 Class = No VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 13499 61.4 Yes 8488 38.6 W = 21987.000 N = 21987 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 49 Class = Yes OCCUPANT_AGE_GROUPS$ = (75+) Class Cases % No 21804 52.3 Yes 19860 47.7 W = 41664.000 N = 41664 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 70 Class = No Class Cases % No 9284 69.0 Yes 4172 31.0 W = 13456.000 N = 13456 YEAR_OF_ACCIDENT <= 1980.50 Terminal Node 71 Class = No Class Cases % No 43 70.5 Yes 18 29.5 W = 61.000 N = 61 AGE_OF_VEHICLE <= 5.50 Terminal Node 72 Class = No Class Cases % No 368 58.0 Yes 266 42.0 W = 634.000 N = 634 AGE_OF_VEHICLE > 5.50 Terminal Node 73 Class = Yes Class Cases % No 38 38.4 Yes 61 61.6 W = 99.000 N = 99 AGE_OF_VEHICLE <= 6.50 Node 78 Class = Yes AGE_OF_VEHICLE <= 5.50 Class Cases % No 406 55.4 Yes 327 44.6 W = 733.000 N = 733 AGE_OF_VEHICLE > 6.50 Terminal Node 74 Class = No Class Cases % No 162 63.0 Yes 95 37.0 W = 257.000 N = 257 YEAR_OF_ACCIDENT > 1980.50 Node 77 Class = No AGE_OF_VEHICLE <= 6.50 Class Cases % No 568 57.4 Yes 422 42.6 W = 990.000 N = 990 YEAR_OF_ACCIDENT <= 1990.50 Node 76 Class = No YEAR_OF_ACCIDENT <= 1980.50 Class Cases % No 611 58.1 Yes 440 41.9 W = 1051.000 N = 1051 YEAR_OF_ACCIDENT > 1990.50 Terminal Node 75 Class = Yes Class Cases % No 1386 46.7 Yes 1584 53.3 W = 2970.000 N = 2970 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 75 Class = Yes YEAR_OF_ACCIDENT <= 1990.50 Class Cases % No 1997 49.7 Yes 2024 50.3 W = 4021.000 N = 4021 MODEL_YEAR_OF_VEHICLE <= 1988.50 Terminal Node 76 Class = No Class Cases % No 954 63.5 Yes 548 36.5 W = 1502.000 N = 1502 MODEL_YEAR_OF_VEHICLE <= 1994.50 Terminal Node 77 Class = Yes Class Cases % No 485 53.7 Yes 419 46.3 W = 904.000 N = 904 MODEL_YEAR_OF_VEHICLE > 1994.50 Terminal Node 78 Class = No Class Cases % No 331 64.6 Yes 181 35.4 W = 512.000 N = 512 MODEL_YEAR_OF_VEHICLE > 1988.50 Node 82 Class = No MODEL_YEAR_OF_VEHICLE <= 1994.50 Class Cases % No 816 57.6 Yes 600 42.4 W = 1416.000 N = 1416 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 81 Class = No MODEL_YEAR_OF_VEHICLE <= 1988.50 Class Cases % No 1770 60.7 Yes 1148 39.3 W = 2918.000 N = 2918 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 79 Class = No Class Cases % No 7443 67.4 Yes 3598 32.6 W = 11041.000 N = 11041 AGE_OF_VEHICLE <= 13.50 Node 80 Class = No OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 9213 66.0 Yes 4746 34.0 W = 13959.000 N = 13959 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 80 Class = Yes Class Cases % No 584 52.1 Yes 536 47.9 W = 1120.000 N = 1120 OCCUPANT_AGE_GROUPS$ = (16 to 20) Terminal Node 81 Class = No Class Cases % No 246 62.8 Yes 146 37.2 W = 392.000 N = 392 AGE_OF_VEHICLE > 13.50 Node 83 Class = Yes OCCUPANT_AGE_GROUPS$ = (12 to 15,21 to 24, 25 to 34,35 to 44) Class Cases % No 830 54.9 Yes 682 45.1 W = 1512.000 N = 1512 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 79 Class = No AGE_OF_VEHICLE <= 13.50 Class Cases % No 10043 64.9 Yes 5428 35.1 W = 15471.000 N = 15471 VEHICLE_WEIGHT_GROUP$ = (< 2451) Node 74 Class = No OCCUPANT_AGE_GROUPS$ = (45 to 54,55 to 64) Class Cases % No 12040 61.8 Yes 7452 38.2 W = 19492.000 N = 19492 YEAR_OF_ACCIDENT <= 2004.50 Terminal Node 82 Class = No Class Cases % No 13382 75.5 Yes 4347 24.5 W = 17729.000 N = 17729 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 83 Class = Yes Class Cases % No 139 37.0 Yes 237 63.0 W = 376.000 N = 376 AGE_OF_VEHICLE <= 16.50 Terminal Node 84 Class = No Class Cases % No 148 69.5 Yes 65 30.5 W = 213.000 N = 213 OCCUPANT_AGE_GROUPS$ = (55 to 64) Terminal Node 85 Class = Yes Class Cases % No 180 52.8 Yes 161 47.2 W = 341.000 N = 341 OCCUPANT_AGE_GROUPS$ = (45 to 54) Terminal Node 86 Class = No Class Cases % No 298 65.5 Yes 157 34.5 W = 455.000 N = 455 AGE_OF_VEHICLE > 16.50 Node 89 Class = No OCCUPANT_AGE_GROUPS$ = (55 to 64) Class Cases % No 478 60.1 Yes 318 39.9 W = 796.000 N = 796 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 88 Class = No AGE_OF_VEHICLE <= 16.50 Class Cases % No 626 62.0 Yes 383 38.0 W = 1009.000 N = 1009 MODEL_YEAR_OF_VEHICLE <= 1992.50 Node 87 Class = Yes CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 765 55.2 Yes 620 44.8 W = 1385.000 N = 1385 MODEL_YEAR_OF_VEHICLE > 1992.50 Terminal Node 87 Class = No Class Cases % No 3023 66.9 Yes 1498 33.1 W = 4521.000 N = 4521 YEAR_OF_ACCIDENT > 2004.50 Node 86 Class = No MODEL_YEAR_OF_VEHICLE <= 1992.50 Class Cases % No 3788 64.1 Yes 2118 35.9 W = 5906.000 N = 5906 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 85 Class = No YEAR_OF_ACCIDENT <= 2004.50 Class Cases % No 17170 72.6 Yes 6465 27.4 W = 23635.000 N = 23635 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 88 Class = No Class Cases % No 54887 81.6 Yes 12350 18.4 W = 67237.000 N = 67237 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 84 Class = No OCCUPANT_AGE_GROUPS$ = (45 to 54,55 to 64) Class Cases % No 72057 79.3 Yes 18815 20.7 W = 90872.000 N = 90872 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 73 Class = No VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 84097 76.2 Yes 26267 23.8 W = 110364.000 N = 110364 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 72 Class = No TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 93381 75.4 Yes 30439 24.6 W = 123820.000 N = 123820 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Terminal Node 89 Class = No Class Cases % No 86853 82.6 Yes 18285 17.4 W = 105138.000 N = 105138 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Node 71 Class = No VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 180234 78.7 Yes 48724 21.3 W = 228958.000 N = 228958 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 90 Class = No Class Cases % No 23988 92.8 Yes 1866 7.2 W = 25854.000 N = 25854 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 70 Class = No TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 204222 80.1 Yes 50590 19.9 W = 254812.000 N = 254812 EXTRICATED_Y_N$ = (No) Node 48 Class = No OCCUPANT_AGE_GROUPS$ = (0 to 11,65 to 74, 75+) Class Cases % No 226026 76.2 Yes 70450 23.8 W = 296476.000 N = 296476 SB_USED_Y_N$ = (Yes) Node 47 Class = No EXTRICATED_Y_N$ = (Yes) Class Cases % No 239669 70.6 Yes 99905 29.4 W = 339574.000 N = 339574 Node 1 Class = Yes SB_USED_Y_N$ = (No) Class Cases % No 338443 56.8 Yes 257279 43.2 W = 595722.000 N = 595722 CART TREE TOPOGRAPHY: DETAILED NODE DATA VIEW
  • 44. 44 EXTRICATED_Y_N$ = (Yes) Terminal Node 1 Class = Yes Class Cases % No 6866 17.4 Yes 32540 82.6 W = 39406.000 N = 39406 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 2 Class = Yes Class Cases % No 3922 32.6 Yes 8113 67.4 W = 12035.000 N = 12035 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Terminal Node 3 Class = Yes Class Cases % No 33 28.2 Yes 84 71.8 W = 117.000 N = 117 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Terminal Node 4 Class = No Class Cases % No 189 63.2 Yes 110 36.8 W = 299.000 N = 299 TRAVEL_SPEED_GROUP$ = (0 to 15) Node 7 Class = Yes OCCUPANT_AGE_GROUPS$ = (0 to 11,75+) Class Cases % No 222 53.4 Yes 194 46.6 W = 416.000 N = 416 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 6 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 4144 33.3 Yes 8307 66.7 W = 12451.000 N = 12451 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 5 Class = Yes Class Cases % No 102 28.3 Yes 258 71.7 W = 360.000 N = 360 OCCUPANT_AGE_GROUPS$ = (65 to 74,...) Terminal Node 6 Class = Yes Class Cases % No 1118 39.8 Yes 1692 60.2 W = 2810.000 N = 2810 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Terminal Node 7 Class = Yes Class Cases % No 1685 48.6 Yes 1779 51.4 W = 3464.000 N = 3464 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 8 Class = Yes Class Cases % No 1 6.7 Yes 14 93.3 W = 15.000 N = 15 YEAR_OF_ACCIDENT <= 1984.50 Terminal Node 9 Class = No Class Cases % No 866 60.2 Yes 573 39.8 W = 1439.000 N = 1439 YEAR_OF_ACCIDENT > 1984.50 Terminal Node 10 Class = Yes Class Cases % No 309 52.8 Yes 276 47.2 W = 585.000 N = 585 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 13 Class = No YEAR_OF_ACCIDENT <= 1984.50 Class Cases % No 1175 58.1 Yes 849 41.9 W = 2024.000 N = 2024 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Node 12 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 1176 57.7 Yes 863 42.3 W = 2039.000 N = 2039 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 11 Class = Yes VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 2861 52.0 Yes 2642 48.0 W = 5503.000 N = 5503 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 10 Class = Yes OCCUPANT_AGE_GROUPS$ = (65 to 74,75+) Class Cases % No 3979 47.9 Yes 4334 52.1 W = 8313.000 N = 8313 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Terminal Node 11 Class = Yes Class Cases % No 73 43.2 Yes 96 56.8 W = 169.000 N = 169 YEAR_OF_ACCIDENT <= 1987.50 Terminal Node 12 Class = No Class Cases % No 452 72.2 Yes 174 27.8 W = 626.000 N = 626 OCCUPANT_AGE_GROUPS$ = (55 to 64,...) Terminal Node 13 Class = Yes Class Cases % No 32 38.1 Yes 52 61.9 W = 84.000 N = 84 OCCUPANT_AGE_GROUPS$ = (45 to 54) Terminal Node 14 Class = No Class Cases % No 43 71.7 Yes 17 28.3 W = 60.000 N = 60 YEAR_OF_ACCIDENT > 1987.50 Node 16 Class = Yes OCCUPANT_AGE_GROUPS$ = (55 to 64,65 to 74) Class Cases % No 75 52.1 Yes 69 47.9 W = 144.000 N = 144 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 15 Class = No YEAR_OF_ACCIDENT <= 1987.50 Class Cases % No 527 68.4 Yes 243 31.6 W = 770.000 N = 770 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 14 Class = No OCCUPANT_AGE_GROUPS$ = (0 to 11,75+) Class Cases % No 600 63.9 Yes 339 36.1 W = 939.000 N = 939 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 9 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 4579 49.5 Yes 4673 50.5 W = 9252.000 N = 9252 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 8 Class = Yes TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 4681 48.7 Yes 4931 51.3 W = 9612.000 N = 9612 YEAR_OF_ACCIDENT <= 1989.50 Node 5 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 8825 40.0 Yes 13238 60.0 W = 22063.000 N = 22063 YEAR_OF_ACCIDENT > 1989.50 Terminal Node 15 Class = Yes Class Cases % No 10251 23.1 Yes 34161 76.9 W = 44412.000 N = 44412 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 4 Class = Yes YEAR_OF_ACCIDENT <= 1989.50 Class Cases % No 19076 28.7 Yes 47399 71.3 W = 66475.000 N = 66475 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 16 Class = Yes Class Cases % No 9939 37.9 Yes 16262 62.1 W = 26201.000 N = 26201 CAR_COMPANY_HQ_REGION$ = (Europe,...) Terminal Node 17 Class = Yes Class Cases % No 8559 36.2 Yes 15094 63.8 W = 23653.000 N = 23653 VEHICLE_WEIGHT_GROUP$ = (< 2451) Terminal Node 18 Class = Yes Class Cases % No 3799 35.6 Yes 6862 64.4 W = 10661.000 N = 10661 OCCUPANT_AGE_GROUPS$ = (35 to 44) Terminal Node 19 Class = Yes Class Cases % No 4842 47.4 Yes 5377 52.6 W = 10219.000 N = 10219 YEAR_OF_ACCIDENT <= 1985.50 Terminal Node 20 Class = Yes Class Cases % No 1561 52.6 Yes 1409 47.4 W = 2970.000 N = 2970 YEAR_OF_ACCIDENT > 1985.50 Terminal Node 21 Class = No Class Cases % No 3905 57.4 Yes 2898 42.6 W = 6803.000 N = 6803 AGE_OF_VEHICLE <= 5.50 Node 26 Class = Yes YEAR_OF_ACCIDENT <= 1985.50 Class Cases % No 5466 55.9 Yes 4307 44.1 W = 9773.000 N = 9773 AGE_OF_VEHICLE > 5.50 Terminal Node 22 Class = Yes Class Cases % No 6862 52.9 Yes 6119 47.1 W = 12981.000 N = 12981 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 25 Class = Yes AGE_OF_VEHICLE <= 5.50 Class Cases % No 12328 54.2 Yes 10426 45.8 W = 22754.000 N = 22754 YEAR_OF_ACCIDENT <= 1998.50 Terminal Node 23 Class = No Class Cases % No 4746 59.3 Yes 3254 40.7 W = 8000.000 N = 8000 YEAR_OF_ACCIDENT > 1998.50 Terminal Node 24 Class = Yes Class Cases % No 767 55.9 Yes 604 44.1 W = 1371.000 N = 1371 MODEL_YEAR_OF_VEHICLE <= 1997.50 Node 28 Class = No YEAR_OF_ACCIDENT <= 1998.50 Class Cases % No 5513 58.8 Yes 3858 41.2 W = 9371.000 N = 9371 MODEL_YEAR_OF_VEHICLE > 1997.50 Terminal Node 25 Class = Yes Class Cases % No 151 47.9 Yes 164 52.1 W = 315.000 N = 315 OCCUPANT_AGE_GROUPS$ = (16 to 20) Node 27 Class = No MODEL_YEAR_OF_VEHICLE <= 1997.50 Class Cases % No 5664 58.5 Yes 4022 41.5 W = 9686.000 N = 9686 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 24 Class = Yes OCCUPANT_AGE_GROUPS$ = (12 to 15,21 to 24, 25 to 34) Class Cases % No 17992 55.5 Yes 14448 44.5 W = 32440.000 N = 32440 YEAR_OF_ACCIDENT <= 2003.50 Node 23 Class = Yes OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 22834 53.5 Yes 19825 46.5 W = 42659.000 N = 42659 YEAR_OF_ACCIDENT > 2003.50 Terminal Node 26 Class = Yes Class Cases % No 3453 42.1 Yes 4757 57.9 W = 8210.000 N = 8210 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 22 Class = Yes YEAR_OF_ACCIDENT <= 2003.50 Class Cases % No 26287 51.7 Yes 24582 48.3 W = 50869.000 N = 50869 CAR_COMPANY_HQ_REGION$ = (USA) Node 21 Class = Yes VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 30086 48.9 Yes 31444 51.1 W = 61530.000 N = 61530 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 20 Class = Yes CAR_COMPANY_HQ_REGION$ = (Europe,Japan,Korea, Other Imports) Class Cases % No 38645 45.4 Yes 46538 54.6 W = 85183.000 N = 85183 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 27 Class = Yes Class Cases % No 110 41.7 Yes 154 58.3 W = 264.000 N = 264 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 28 Class = No Class Cases % No 67 69.1 Yes 30 30.9 W = 97.000 N = 97 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 32 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35) Class Cases % No 177 49.0 Yes 184 51.0 W = 361.000 N = 361 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 29 Class = No Class Cases % No 689 64.2 Yes 385 35.8 W = 1074.000 N = 1074 YEAR_OF_ACCIDENT <= 2007.50 Node 31 Class = No OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 866 60.3 Yes 569 39.7 W = 1435.000 N = 1435 YEAR_OF_ACCIDENT > 2007.50 Terminal Node 30 Class = Yes Class Cases % No 89 40.6 Yes 130 59.4 W = 219.000 N = 219 CAR_COMPANY_HQ_REGION$ = (Europe,...) Node 30 Class = No YEAR_OF_ACCIDENT <= 2007.50 Class Cases % No 955 57.7 Yes 699 42.3 W = 1654.000 N = 1654 CAR_COMPANY_HQ_REGION$ = (Other Impo...) Terminal Node 31 Class = No Class Cases % No 3202 73.1 Yes 1180 26.9 W = 4382.000 N = 4382 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 29 Class = No CAR_COMPANY_HQ_REGION$ = (Europe,Japan,Korea) Class Cases % No 4157 68.9 Yes 1879 31.1 W = 6036.000 N = 6036 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 19 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 42802 46.9 Yes 48417 53.1 W = 91219.000 N = 91219 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 32 Class = Yes Class Cases % No 93 34.2 Yes 179 65.8 W = 272.000 N = 272 MODEL_YEAR_OF_VEHICLE <= 1977.50 Terminal Node 33 Class = No Class Cases % No 5613 66.4 Yes 2843 33.6 W = 8456.000 N = 8456 YEAR_OF_ACCIDENT <= 1985.50 Terminal Node 34 Class = No Class Cases % No 2365 62.8 Yes 1400 37.2 W = 3765.000 N = 3765 OCCUPANT_AGE_GROUPS$ = (21 to 24,...) Terminal Node 35 Class = Yes Class Cases % No 1586 53.3 Yes 1391 46.7 W = 2977.000 N = 2977 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 36 Class = No Class Cases % No 514 64.3 Yes 286 35.8 W = 800.000 N = 800 YEAR_OF_ACCIDENT > 1985.50 Node 39 Class = Yes OCCUPANT_AGE_GROUPS$ = (21 to 24,25 to 34, 35 to 44) Class Cases % No 2100 55.6 Yes 1677 44.4 W = 3777.000 N = 3777 MODEL_YEAR_OF_VEHICLE > 1977.50 Node 38 Class = No YEAR_OF_ACCIDENT <= 1985.50 Class Cases % No 4465 59.2 Yes 3077 40.8 W = 7542.000 N = 7542 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 37 Class = No MODEL_YEAR_OF_VEHICLE <= 1977.50 Class Cases % No 10078 63.0 Yes 5920 37.0 W = 15998.000 N = 15998 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 36 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 10171 62.5 Yes 6099 37.5 W = 16270.000 N = 16270 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 37 Class = No Class Cases % No 3680 72.7 Yes 1385 27.3 W = 5065.000 N = 5065 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 35 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 13851 64.9 Yes 7484 35.1 W = 21335.000 N = 21335 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 38 Class = No Class Cases % No 1328 84.6 Yes 241 15.4 W = 1569.000 N = 1569 YEAR_OF_ACCIDENT <= 1993.50 Node 34 Class = No TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 15179 66.3 Yes 7725 33.7 W = 22904.000 N = 22904 MODEL_YEAR_OF_VEHICLE <= 1976.50 Terminal Node 39 Class = No Class Cases % No 80 61.5 Yes 50 38.5 W = 130.000 N = 130 MODEL_YEAR_OF_VEHICLE > 1976.50 Terminal Node 40 Class = Yes Class Cases % No 947 37.5 Yes 1575 62.5 W = 2522.000 N = 2522 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 42 Class = Yes MODEL_YEAR_OF_VEHICLE <= 1976.50 Class Cases % No 1027 38.7 Yes 1625 61.3 W = 2652.000 N = 2652 MODEL_YEAR_OF_VEHICLE <= 1979.50 Terminal Node 41 Class = No Class Cases % No 357 64.8 Yes 194 35.2 W = 551.000 N = 551 YEAR_OF_ACCIDENT <= 1995.50 Terminal Node 42 Class = No Class Cases % No 321 59.3 Yes 220 40.7 W = 541.000 N = 541 YEAR_OF_ACCIDENT > 1995.50 Terminal Node 43 Class = Yes Class Cases % No 691 52.0 Yes 638 48.0 W = 1329.000 N = 1329 MODEL_YEAR_OF_VEHICLE > 1979.50 Node 46 Class = Yes YEAR_OF_ACCIDENT <= 1995.50 Class Cases % No 1012 54.1 Yes 858 45.9 W = 1870.000 N = 1870 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 45 Class = Yes MODEL_YEAR_OF_VEHICLE <= 1979.50 Class Cases % No 1369 56.5 Yes 1052 43.5 W = 2421.000 N = 2421 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 44 Class = No Class Cases % No 257 66.8 Yes 128 33.2 W = 385.000 N = 385 YEAR_OF_ACCIDENT <= 2000.50 Node 44 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 1626 57.9 Yes 1180 42.1 W = 2806.000 N = 2806 YEAR_OF_ACCIDENT > 2000.50 Terminal Node 45 Class = Yes Class Cases % No 1821 46.8 Yes 2067 53.2 W = 3888.000 N = 3888 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 43 Class = Yes YEAR_OF_ACCIDENT <= 2000.50 Class Cases % No 3447 51.5 Yes 3247 48.5 W = 6694.000 N = 6694 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 41 Class = Yes OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 4474 47.9 Yes 4872 52.1 W = 9346.000 N = 9346 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 46 Class = No Class Cases % No 438 73.4 Yes 159 26.6 W = 597.000 N = 597 YEAR_OF_ACCIDENT > 1993.50 Node 40 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55) Class Cases % No 4912 49.4 Yes 5031 50.6 W = 9943.000 N = 9943 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 33 Class = No YEAR_OF_ACCIDENT <= 1993.50 Class Cases % No 20091 61.2 Yes 12756 38.8 W = 32847.000 N = 32847 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Node 18 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 62893 50.7 Yes 61173 49.3 W = 124066.000 N = 124066 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 17 Class = Yes TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 72832 48.5 Yes 77435 51.5 W = 150267.000 N = 150267 EXTRICATED_Y_N$ = (No) Node 3 Class = Yes OCCUPANT_AGE_GROUPS$ = (0 to 11,45 to 54, 55 to 64,65 to 74, 75+) Class Cases % No 91908 42.4 Yes 124834 57.6 W = 216742.000 N = 216742 SB_USED_Y_N$ = (No) Node 2 Class = Yes EXTRICATED_Y_N$ = (Yes) Class Cases % No 98774 38.6 Yes 157374 61.4 W = 256148.000 N = 256148 EXTRICATED_Y_N$ = (Yes) Terminal Node 47 Class = Yes Class Cases % No 13643 31.7 Yes 29455 68.3 W = 43098.000 N = 43098 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Terminal Node 48 Class = Yes Class Cases % No 3290 36.3 Yes 5770 63.7 W = 9060.000 N = 9060 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 49 Class = Yes Class Cases % No 4541 46.0 Yes 5334 54.0 W = 9875.000 N = 9875 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 50 Class = No Class Cases % No 474 63.9 Yes 268 36.1 W = 742.000 N = 742 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 51 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 5015 47.2 Yes 5602 52.8 W = 10617.000 N = 10617 OCCUPANT_AGE_GROUPS$ = (75+) Node 50 Class = Yes VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 8305 42.2 Yes 11372 57.8 W = 19677.000 N = 19677 VEHICLE_WEIGHT_GROUP$ = (< 2451) Terminal Node 51 Class = Yes Class Cases % No 612 38.3 Yes 987 61.7 W = 1599.000 N = 1599 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Terminal Node 52 Class = Yes Class Cases % No 857 50.8 Yes 831 49.2 W = 1688.000 N = 1688 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 53 Class = No Class Cases % No 108 64.3 Yes 60 35.7 W = 168.000 N = 168 SEX$ = (Female) Node 57 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 965 52.0 Yes 891 48.0 W = 1856.000 N = 1856 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 54 Class = Yes Class Cases % No 127 53.4 Yes 111 46.6 W = 238.000 N = 238 AGE_OF_VEHICLE <= 6.50 Terminal Node 55 Class = No Class Cases % No 151 61.9 Yes 93 38.1 W = 244.000 N = 244 AGE_OF_VEHICLE > 6.50 Terminal Node 56 Class = Yes Class Cases % No 104 48.6 Yes 110 51.4 W = 214.000 N = 214 CAR_COMPANY_HQ_REGION$ = (Japan,...) Node 60 Class = Yes AGE_OF_VEHICLE <= 6.50 Class Cases % No 255 55.7 Yes 203 44.3 W = 458.000 N = 458 CAR_COMPANY_HQ_REGION$ = (Europe,...) Terminal Node 57 Class = No Class Cases % No 1818 64.9 Yes 985 35.1 W = 2803.000 N = 2803 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 59 Class = No CAR_COMPANY_HQ_REGION$ = (Japan,Other Imports) Class Cases % No 2073 63.6 Yes 1188 36.4 W = 3261.000 N = 3261 SEX$ = (*,Male) Node 58 Class = No TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 2200 62.9 Yes 1299 37.1 W = 3499.000 N = 3499 YEAR_OF_ACCIDENT <= 2003.50 Node 56 Class = No SEX$ = (Female) Class Cases % No 3165 59.1 Yes 2190 40.9 W = 5355.000 N = 5355 YEAR_OF_ACCIDENT > 2003.50 Terminal Node 58 Class = Yes Class Cases % No 825 47.1 Yes 925 52.9 W = 1750.000 N = 1750 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 55 Class = Yes YEAR_OF_ACCIDENT <= 2003.50 Class Cases % No 3990 56.2 Yes 3115 43.8 W = 7105.000 N = 7105 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 54 Class = Yes VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 4602 52.9 Yes 4102 47.1 W = 8704.000 N = 8704 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 59 Class = No Class Cases % No 407 76.5 Yes 125 23.5 W = 532.000 N = 532 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 53 Class = Yes TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 5009 54.2 Yes 4227 45.8 W = 9236.000 N = 9236 TRAVEL_SPEED_GROUP$ = (> 75) Terminal Node 60 Class = Yes Class Cases % No 23 41.8 Yes 32 58.2 W = 55.000 N = 55 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Terminal Node 61 Class = No Class Cases % No 4383 68.0 Yes 2060 32.0 W = 6443.000 N = 6443 AGE_OF_VEHICLE <= 8.50 Node 64 Class = No TRAVEL_SPEED_GROUP$ = (> 75) Class Cases % No 4406 67.8 Yes 2092 32.2 W = 6498.000 N = 6498 YEAR_OF_ACCIDENT <= 1992.50 Terminal Node 62 Class = No Class Cases % No 166 72.2 Yes 64 27.8 W = 230.000 N = 230 MODEL_YEAR_OF_VEHICLE <= 2001.50 Terminal Node 63 Class = Yes Class Cases % No 313 47.9 Yes 340 52.1 W = 653.000 N = 653 MODEL_YEAR_OF_VEHICLE > 2001.50 Terminal Node 64 Class = No Class Cases % No 272 58.6 Yes 192 41.4 W = 464.000 N = 464 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Node 68 Class = Yes MODEL_YEAR_OF_VEHICLE <= 2001.50 Class Cases % No 585 52.4 Yes 532 47.6 W = 1117.000 N = 1117 TRAVEL_SPEED_GROUP$ = (16 to 35) Terminal Node 65 Class = No Class Cases % No 73 62.9 Yes 43 37.1 W = 116.000 N = 116 SEX$ = (*,Female) Node 67 Class = Yes TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 658 53.4 Yes 575 46.6 W = 1233.000 N = 1233 AGE_OF_VEHICLE <= 14.50 Terminal Node 66 Class = No Class Cases % No 1068 61.2 Yes 677 38.8 W = 1745.000 N = 1745 AGE_OF_VEHICLE > 14.50 Terminal Node 67 Class = Yes Class Cases % No 336 54.8 Yes 277 45.2 W = 613.000 N = 613 SEX$ = (Male) Node 69 Class = No AGE_OF_VEHICLE <= 14.50 Class Cases % No 1404 59.5 Yes 954 40.5 W = 2358.000 N = 2358 YEAR_OF_ACCIDENT > 1992.50 Node 66 Class = No SEX$ = (*,Female) Class Cases % No 2062 57.4 Yes 1529 42.6 W = 3591.000 N = 3591 AGE_OF_VEHICLE > 8.50 Node 65 Class = No YEAR_OF_ACCIDENT <= 1992.50 Class Cases % No 2228 58.3 Yes 1593 41.7 W = 3821.000 N = 3821 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 63 Class = No AGE_OF_VEHICLE <= 8.50 Class Cases % No 6634 64.3 Yes 3685 35.7 W = 10319.000 N = 10319 VEHICLE_WEIGHT_GROUP$ = (3951 to 47...) Terminal Node 68 Class = No Class Cases % No 1237 72.7 Yes 465 27.3 W = 1702.000 N = 1702 TRAVEL_SPEED_GROUP$ = (16 to 35,...) Node 62 Class = No VEHICLE_WEIGHT_GROUP$ = (3201 to 3950) Class Cases % No 7871 65.5 Yes 4150 34.5 W = 12021.000 N = 12021 TRAVEL_SPEED_GROUP$ = (0 to 15) Terminal Node 69 Class = No Class Cases % No 619 84.8 Yes 111 15.2 W = 730.000 N = 730 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Node 61 Class = No TRAVEL_SPEED_GROUP$ = (16 to 35,36 to 55, 56 to 75,> 75) Class Cases % No 8490 66.6 Yes 4261 33.4 W = 12751.000 N = 12751 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 52 Class = No VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 13499 61.4 Yes 8488 38.6 W = 21987.000 N = 21987 OCCUPANT_AGE_GROUPS$ = (0 to 11,...) Node 49 Class = Yes OCCUPANT_AGE_GROUPS$ = (75+) Class Cases % No 21804 52.3 Yes 19860 47.7 W = 41664.000 N = 41664 TRAVEL_SPEED_GROUP$ = (56 to 75,...) Terminal Node 70 Class = No Class Cases % No 9284 69.0 Yes 4172 31.0 W = 13456.000 N = 13456 YEAR_OF_ACCIDENT <= 1980.50 Terminal Node 71 Class = No Class Cases % No 43 70.5 Yes 18 29.5 W = 61.000 N = 61 AGE_OF_VEHICLE <= 5.50 Terminal Node 72 Class = No Class Cases % No 368 58.0 Yes 266 42.0 W = 634.000 N = 634 AGE_OF_VEHICLE > 5.50 Terminal Node 73 Class = Yes Class Cases % No 38 38.4 Yes 61 61.6 W = 99.000 N = 99 AGE_OF_VEHICLE <= 6.50 Node 78 Class = Yes AGE_OF_VEHICLE <= 5.50 Class Cases % No 406 55.4 Yes 327 44.6 W = 733.000 N = 733 AGE_OF_VEHICLE > 6.50 Terminal Node 74 Class = No Class Cases % No 162 63.0 Yes 95 37.0 W = 257.000 N = 257 YEAR_OF_ACCIDENT > 1980.50 Node 77 Class = No AGE_OF_VEHICLE <= 6.50 Class Cases % No 568 57.4 Yes 422 42.6 W = 990.000 N = 990 YEAR_OF_ACCIDENT <= 1990.50 Node 76 Class = No YEAR_OF_ACCIDENT <= 1980.50 Class Cases % No 611 58.1 Yes 440 41.9 W = 1051.000 N = 1051 YEAR_OF_ACCIDENT > 1990.50 Terminal Node 75 Class = Yes Class Cases % No 1386 46.7 Yes 1584 53.3 W = 2970.000 N = 2970 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 75 Class = Yes YEAR_OF_ACCIDENT <= 1990.50 Class Cases % No 1997 49.7 Yes 2024 50.3 W = 4021.000 N = 4021 MODEL_YEAR_OF_VEHICLE <= 1988.50 Terminal Node 76 Class = No Class Cases % No 954 63.5 Yes 548 36.5 W = 1502.000 N = 1502 MODEL_YEAR_OF_VEHICLE <= 1994.50 Terminal Node 77 Class = Yes Class Cases % No 485 53.7 Yes 419 46.3 W = 904.000 N = 904 MODEL_YEAR_OF_VEHICLE > 1994.50 Terminal Node 78 Class = No Class Cases % No 331 64.6 Yes 181 35.4 W = 512.000 N = 512 MODEL_YEAR_OF_VEHICLE > 1988.50 Node 82 Class = No MODEL_YEAR_OF_VEHICLE <= 1994.50 Class Cases % No 816 57.6 Yes 600 42.4 W = 1416.000 N = 1416 OCCUPANT_AGE_GROUPS$ = (35 to 44) Node 81 Class = No MODEL_YEAR_OF_VEHICLE <= 1988.50 Class Cases % No 1770 60.7 Yes 1148 39.3 W = 2918.000 N = 2918 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 79 Class = No Class Cases % No 7443 67.4 Yes 3598 32.6 W = 11041.000 N = 11041 AGE_OF_VEHICLE <= 13.50 Node 80 Class = No OCCUPANT_AGE_GROUPS$ = (35 to 44) Class Cases % No 9213 66.0 Yes 4746 34.0 W = 13959.000 N = 13959 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 80 Class = Yes Class Cases % No 584 52.1 Yes 536 47.9 W = 1120.000 N = 1120 OCCUPANT_AGE_GROUPS$ = (16 to 20) Terminal Node 81 Class = No Class Cases % No 246 62.8 Yes 146 37.2 W = 392.000 N = 392 AGE_OF_VEHICLE > 13.50 Node 83 Class = Yes OCCUPANT_AGE_GROUPS$ = (12 to 15,21 to 24, 25 to 34,35 to 44) Class Cases % No 830 54.9 Yes 682 45.1 W = 1512.000 N = 1512 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 79 Class = No AGE_OF_VEHICLE <= 13.50 Class Cases % No 10043 64.9 Yes 5428 35.1 W = 15471.000 N = 15471 VEHICLE_WEIGHT_GROUP$ = (< 2451) Node 74 Class = No OCCUPANT_AGE_GROUPS$ = (45 to 54,55 to 64) Class Cases % No 12040 61.8 Yes 7452 38.2 W = 19492.000 N = 19492 YEAR_OF_ACCIDENT <= 2004.50 Terminal Node 82 Class = No Class Cases % No 13382 75.5 Yes 4347 24.5 W = 17729.000 N = 17729 CAR_COMPANY_HQ_REGION$ = (Japan,...) Terminal Node 83 Class = Yes Class Cases % No 139 37.0 Yes 237 63.0 W = 376.000 N = 376 AGE_OF_VEHICLE <= 16.50 Terminal Node 84 Class = No Class Cases % No 148 69.5 Yes 65 30.5 W = 213.000 N = 213 OCCUPANT_AGE_GROUPS$ = (55 to 64) Terminal Node 85 Class = Yes Class Cases % No 180 52.8 Yes 161 47.2 W = 341.000 N = 341 OCCUPANT_AGE_GROUPS$ = (45 to 54) Terminal Node 86 Class = No Class Cases % No 298 65.5 Yes 157 34.5 W = 455.000 N = 455 AGE_OF_VEHICLE > 16.50 Node 89 Class = No OCCUPANT_AGE_GROUPS$ = (55 to 64) Class Cases % No 478 60.1 Yes 318 39.9 W = 796.000 N = 796 CAR_COMPANY_HQ_REGION$ = (Europe,USA) Node 88 Class = No AGE_OF_VEHICLE <= 16.50 Class Cases % No 626 62.0 Yes 383 38.0 W = 1009.000 N = 1009 MODEL_YEAR_OF_VEHICLE <= 1992.50 Node 87 Class = Yes CAR_COMPANY_HQ_REGION$ = (Japan,Korea,Other Imports) Class Cases % No 765 55.2 Yes 620 44.8 W = 1385.000 N = 1385 MODEL_YEAR_OF_VEHICLE > 1992.50 Terminal Node 87 Class = No Class Cases % No 3023 66.9 Yes 1498 33.1 W = 4521.000 N = 4521 YEAR_OF_ACCIDENT > 2004.50 Node 86 Class = No MODEL_YEAR_OF_VEHICLE <= 1992.50 Class Cases % No 3788 64.1 Yes 2118 35.9 W = 5906.000 N = 5906 OCCUPANT_AGE_GROUPS$ = (45 to 54,...) Node 85 Class = No YEAR_OF_ACCIDENT <= 2004.50 Class Cases % No 17170 72.6 Yes 6465 27.4 W = 23635.000 N = 23635 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Terminal Node 88 Class = No Class Cases % No 54887 81.6 Yes 12350 18.4 W = 67237.000 N = 67237 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 84 Class = No OCCUPANT_AGE_GROUPS$ = (45 to 54,55 to 64) Class Cases % No 72057 79.3 Yes 18815 20.7 W = 90872.000 N = 90872 TRAVEL_SPEED_GROUP$ = (36 to 55) Node 73 Class = No VEHICLE_WEIGHT_GROUP$ = (< 2451) Class Cases % No 84097 76.2 Yes 26267 23.8 W = 110364.000 N = 110364 VEHICLE_WEIGHT_GROUP$ = (2451 to 32...) Node 72 Class = No TRAVEL_SPEED_GROUP$ = (56 to 75,> 75) Class Cases % No 93381 75.4 Yes 30439 24.6 W = 123820.000 N = 123820 VEHICLE_WEIGHT_GROUP$ = (3201 to 39...) Terminal Node 89 Class = No Class Cases % No 86853 82.6 Yes 18285 17.4 W = 105138.000 N = 105138 TRAVEL_SPEED_GROUP$ = (36 to 55,...) Node 71 Class = No VEHICLE_WEIGHT_GROUP$ = (2451 to 3200,< 2451) Class Cases % No 180234 78.7 Yes 48724 21.3 W = 228958.000 N = 228958 TRAVEL_SPEED_GROUP$ = (0 to 15,...) Terminal Node 90 Class = No Class Cases % No 23988 92.8 Yes 1866 7.2 W = 25854.000 N = 25854 OCCUPANT_AGE_GROUPS$ = (12 to 15,...) Node 70 Class = No TRAVEL_SPEED_GROUP$ = (36 to 55,56 to 75, > 75) Class Cases % No 204222 80.1 Yes 50590 19.9 W = 254812.000 N = 254812 EXTRICATED_Y_N$ = (No) Node 48 Class = No OCCUPANT_AGE_GROUPS$ = (0 to 11,65 to 74, 75+) Class Cases % No 226026 76.2 Yes 70450 23.8 W = 296476.000 N = 296476 SB_USED_Y_N$ = (Yes) Node 47 Class = No EXTRICATED_Y_N$ = (Yes) Class Cases % No 239669 70.6 Yes 99905 29.4 W = 339574.000 N = 339574 Node 1 Class = Yes SB_USED_Y_N$ = (No) Class Cases % No 338443 56.8 Yes 257279 43.2 W = 595722.000 N = 595722 CART TREE TOPOGRAPHY: DETAILED NODE DATA VIEW
  • 45. 45 HOTSPOT REPORT: TREE NODE #S WITH THE HIGHEST % OF FATALITIES
  • 46. 46 HOTSPOT REPORT: TREE NODE #S WITH THE LOWEST % OF FATALITIES
  • 47. 47 MARS (MULTIVARIATE ADAPTIVE REGRESSION SPLINES) AUTOMATED REGRESSION MODELING With missing value (mis) importance rankings Variable Importance rankings as part of the analysis summary:
  • 48. 48Fatality rates increase as vehicles age High vehicle age TREENET AUTOMATED 3D CHARTING OF ALL POSSIBLE FACTOR COMBINATIONS Treenet 3D charts Datafit charts with RSq & p-values for the 3D surfaces Fatality rates increase 70% with higher vehicle age + occupant age vs. new vehicles and young occupants High vehicle + Occupant age
  • 49. 49 UNSUPERVISED VEHICLE ACCIDENT RESEARCH: SEEKING FOR NEW THEORIES • Steel and other materials lose 50%+ of their strength over time • Corrosion adds to crash risks • Aging cars collapse more in a crash as they age • Older passengers and / or aging cars are a risky mix • Heavy vehicles are generally safer • Vehicle Star ratings fade over time • Multiple confounding factors decide who lives and who dies • 18 other high income countries have lower traffic accident death rates than the USA. Why? Research Question: “What’s going on here?”
  • 50. 50 CONCLUSION • Robust applications of ML techniques can safeguard against superficial analysis and analysis paralysis • ML can offer the problem- solvers new guidance and hotspot detection with its automated deep analysis and exhaustive data stratification • In unsupervised analysis mode, ML can offer new insights and theories for future research consideration
  • 51. 51 Q & A Any Questions? Author contact information: David.Patrishkoff@CascadeEffects.com