my model genuines.

1

iot,
my
small
ai
VERSION,
scope
of
analytics
2015

learning,
thinking,
practicing

Any
other
thoughts
please
feel
free
to
contact,
luxiaoteng0
(at)
gmail
(dot)
com

Data
has
no
shadow.
Whatever
it’s
sculptured,
it’s.

As
long
as
it
has
landed
to
the
completion
of
model-‐100,
TENG’s
self-‐assignment

on
modeling
techniques
self-‐learning,
predictive
analytics
related,
TENG
is
able

to
decipher
hybrid
data
solution,
which
is
crossing
of
generic
model
techniques

such
as
familiar
hierarchical
cluster
and
a
lot,
plus
domain
knowledge
i.e.
bank,

retail,
mobile
etc.
as
well
as
it
empowers
advanced
data
mining
applied
with

machine
learning.

It
has
built
into
three
dimensional
data
analysis
matrix.

Inside
the
genres,
TENG
is
unique
to
design
data
analysis
models,
not
only

learning
from
traditional
analysis
modules,
but
also
consolidate
multiple

techniques
to
address
realistic
business
needs.
It
rolls
out
a
positive
loop
that

algorithms
could
be
best
fit
into
where
it’s
needed
for
performance
improving

related
to
well
recognized
business
situation.
<data
UNME>
converge
the
usages

of
text
mining,
semantic
analysis
and
recommendation
system.
Each
of
these

analytics
modules
have
been
evidently
extracted
from
high
profile
source.
It

consolidates
movie/drama/program
viewingship
pattern
through
multiple

platforms
x
devices.
Individual
could
be
classified
by
tag
through
the
output

scoring
counts.
This
process
is
consistently
applied
with
supervised
learning
per

Google’s
tagged
word
embedding
methodology.
Brand
could
leverage
the

viewing
score
to
plan
optimum
brand
awareness
as
customer-‐centric
driven.

Furthermore,
it
renders
in
the
range
of
multiple
channel
intelligences
because
it

2

makes
the
variables
standardization
available
across
online
video,
time-‐shifted

TV,
social
media
plus
influences
from
movie
on-‐air.

Case
Study,
NLP
adoption,

A
famous
early
example
of
the
use
of
cognitive
technology
to
improve
a
product
offering
is
the

recommendation
feature
of
the
Netflix
online
movie
rental
service,
which
uses
machine
learning
to

predict
which
movies
a
customer
will
like.
This
feature
has
had
a
significant
impact
on
customers’

use
of
the
service;
it
accounts
for
as
much
as
75
percent
of
Netflix
usage.

To
improve
marketing
and
customer
service,
BBVA
Compass
bank
uses
a
social
media
sentiment

monitoring
tool
to
track
and
understand
what
consumers
are
saying
about
the
bank
and
its

competitors.
The
tool,
which
incorporates
natural
language
processing
technology,
automatically

identifies
salient
topics
of
consumer
chatter
and
the
sentiments
surrounding
those
topics.
These

insights
influence
the
bank’s
decisions
on
setting
fees
and
offering
consumer
perks,
and
how

customer
service
representatives
should
respond
to
certain
customer
inquiries
about
services
and

fees.22

Source:
Deloitte
Review

Seed
Program
(1)#
<data
UNME>
follows
the
working
principle,

Since
content
consumption
dominates
online
video
viewing,
it
is
possible
to

reinvent
viewingship
measurement
according
to
preferences
of
the
programs.

With
the
chosen
variables,
targeting
market
could
be
defined
whereas
brands

attribute
according
to
the
methods
of
content
connection
instead
of
digital

metrics
only.
It
makes
feasible
translation
with
data
works
out
virtually
channel

mix
strategy.
So
does
it
fulfill
end-‐to-‐end
data
solution
about
performance,

segmentation,
engagement.

.
Track
the
influences
generated
from
each
viewingship;

.
It
carries
in
defined
framework;

.
Viewingship
is
measured
as
a
link
to
the
contents
counted
by
chosen
variables;

.
Besides
monitoring
actions
on
programs,
it
also
has
advantage
to
decipher

similar
program
influences
differentiated
in
various
platforms;

.
In
other
words,
the
platform
performance
could
be
taken
as
the
adoption
of

variety
of
programs
which
associate
to
the
observing
variables;

.
The
preferences
on
programs
reflect
the
quality
of
users
and
it’s
conveniently
to

quantify
users
attributions.

More
releases
that
social
network
analysis
links
to
utilize
tangible
conditional

probability
predictive
method.

In
the
mean
time,
it
has
the
other
ascendencies.

First,
it
could
be
synergized
with
well-‐established
segmentation.
According
to
log

linear
model,
target
groups’
viewing
habit
could
be
drawn
simultaneously
with

pre-‐option
affinity
variables,
besides
viewingship
data,
reviews,
favors,
there
are

more
like
program
type,
host,
director,
actor,
geo,
timing,
devices.
With
time

being,
it’s
obvious
to
maximize
the
significance
bonding
between
brand

3

consumer
features
and
viewing
indexation.
Consequently,
the
value
of
data

insight
could
be
further
solicited
on
brand
awareness
optimization.
How
much
it

correlates
to
the
revenues?
Only
when
this
solid
awareness
combination
could

be
visualized,
the
equation
about
brand,
consumer
interaction,
learned
from
data

analysis
guru
Dawn
Iacobucci
[fig.1]
can
be
measured
in
a
learning
path

wherever
it’s
under
the
bigger
background
as
forming
intelligent
enterprise

[fig.2].

[fig.1]

Ad
exposure
!
Brand
awareness
!
Attitude
~
ad
!
Buying
intention
!

Purchase

Ad
exposure
!
Brand
awareness
!
Attitude
~
brand
!
Buying
intention
!

Purchase

Price
!
Buying
intention

[fig.2]

Have
to
thank
for
references
being
with
theoretical
proofs,

1. Natural
Language
Processing
(almost)
from
Scratch,
Journal
of
Machine
Learning
Research
1
(2000)
1-‐48,

Ronan
Collobert,
Jason
Weston,
L
́eon
Bottou,
Michael
Karlen,
Koray
Kavukcuoglu,
Pavel
Kuksa.

2. Deep
learning
&
NLP
-‐
Graphs
to
the
Rescue,
(or
not
yet!),
Stockholm,
Sics,
October
21
2014,
Roelof
Pieters,

KTH/CSC,
Graph
Technologies
R&D
-‐

3. Deep
learning
for
NLP,
An
Introduction
to
Neural
Word
Embeddings*
and
some
more
fun
stuff…,
KTH,

December
4,
2014,
Roelof
Pieters

PhD
candidate
KTH/CSC
CIO/CTO
Feeda
AB

4. Three
New
Graphical
Models
for
Statistical
Language
Modelling,
Andriy
Mnih,
Geoffrey
Hinton,
Department
of

Computer
Science,
University
of
Toronto,
Canada

4

5. Statistical
Language
Models
Based
on
Neural
Networks,
Google,
Moutain
View,
2nd
April
2012,
Tomas
Mikolov,

Strategies
for
training
large
scale
neural
network
language
models,
Microsoft
Research,
Redmond,
WA,
USA,

Toma
́sˇ
Mikolov
#1,
Anoop
Deoras
∗2,
Daniel
Povey
†3,
Luka
́sˇ
Burget
#4,
Jan
“Honza”
Cˇ
ernocky
́
#5
#
Brno

University
of
Technology,
Speech@FIT,
Brno,
Czech
Republic

6. DeViSE:
A
Deep
Visual-‐Semantic
Embedding
Model,
Google,
Inc.
Mountain
View,
CA,
USA,
Andrea
Frome*,
Greg

S.
Corrado*,
Jonathon
Shlens*,
Samy
Bengio
Jeffrey
Dean,
Marc’Aurelio
Ranzato,
Tomas
Mikolov

Whatever
it’s
probably
not
a
game-‐changer,
it
still
transforms
to
model
designer

and
pursues
my
own
best
practices.
Thank
you.

Is
it
formidable?
What
if
it’s
just
thy
truth
too
much.
Who
just
plays

autocorrelation?
Unsuspected
social
site
easily
becomes
assistive.
How
about

time-‐shifted
TV?
It
gears
up
the
follower
in
ahead.
Is
it
awesome?
Whenever

think
about
brand
tailors
your
own
broadcasting
station,
secret
recipe
is
about

virtual
channel
*
type
*
program.
Please
contact
Teng,
it
will
have
hypothesis,

experiments,
data
analysis
methodology
decipher,
insight
report
and
your

brand’s
quotient,
everything
in
the
case
looking
at

viewhingship/clicks/comments
as
the
series
of
actions
caused
by
users.
There
is

the
correlation
between
the
certain
group
of
viewers
and
their
preferences.

Preferences
have
been
discerned
by
the
program
viewingshing
habit.
How
about

brand
overarches
the
findings
in
veins,
thus
exert
the
inherent
influences
into

viewers
who
has
been
identified
as
worth
of
targeting
according
to
previous

program
classifications.
[fig.3].

As
long
as
observing
on
conditional
probability
merged
with
social
mining,

unstructured
data
utilization
could
be
standardized
firstly
in
the
sequence
to

apply
hierarchy
bayesian.
It
is
consistent
to
Seed
Program
(2)#
e-‐chainTM
that
the

extreme
focus
on
top
three
conversions
in
data
stream,
e-‐commerce,
engine,

email.
In
tradition,
it
veins
into
impression,
click,
acquisition,
transaction.
After

developing
word
bag,
2
algorithms
Gibbs
Sampling
and
Metropolis
Hasting
are

particularly
useful
to
draw
posterior
distribution.
This
is
very
crucial
conclusion

drawn
from
my
some
researches
and
theoretical
learning.
Probably
it’s
familiar

by
others,
but
for
me
it
takes
some
efforts.
It
has
4
strengths
by
adopting
of
this

data
solution
from
my
point
of
view,
1.
It
has
the
completed
achievement
of
data

tracking
in
each
outcome
layer;
2.
Follows
contextual
measurement
without

losing
focus
90%
conversions
regarding
of
e-‐business
backbone;
3.
It
works

under
hive
philosophy.
It
involves
the
touch
points
as
beneficial
extensions;
4.

It’s
effective
to
merge
with
other
analysis
module
i.e.
brand
simulation
in

advance.
It’s
able
to
utilize
Deep
Learning
as
far
as
there
are
feasibly
55
layers

applied
in
Google’s
analysis
that
I
read
from
one
article
before
in
somewhere.

[fig.3]

. about
Spark
Internet
Button
/
SIB*,
{if
this,
then
that.}
data
analysis
integration
inside
<data
UNME>
applied
with
myself
some
reliable
proofs
in
data
analysis
theories,
[fig.3]

5

other
scenario
soonest
specifically
for
CIO
references.

What
is
differentiations
between
supervised
learning
and
unsupervised

learning
in
terms
of
social
mining.

• A
new
study
has
revealed
a
way
to
do
sentiment
analysis
on
a
large
number
of
social

media
images
using
unsupervised
learning.

• Unsupervised
learning
in
AI
is
a
step
above
supervised
learning
where
machines
have
to

work
with
unlabelled
data,
observe
and
make
sense
of
it,
and
provide
an
outcome.

Supervised
learning,
on
the
other
hand,
gives
machines
labelled
data
or
examples
to

learn
from
when
carrying
out
certain
tasks
such
as
classifying
an
object
or
predicting

future
outcomes.
The
study,
Unsupervised
Sentiment
Analysis
for
Social
Media
Images,

was
released
as
part
of
the
International
Joint
Conference
on
Artificial
Intelligence
in

Argentina
this
week.
It
reveals
a
novel
framework,
called
Unsupervised
Sentiment

Analysis
(USEA),
that
uses
both
textual
and
visual
data
in
a
single
model
for
learning.

• Images
from
social
media
sites
offer
rich
data
to
work
with
when
doing
sentiment

analysis.
However,
manually
labelling
millions
of
images
is
too
labour-‐
and
time-‐
intensive,
meaning
this
data
often
goes
untapped.
This
is
why
the
study's
authors

focused
their
efforts
on
unsupervised
learning.

• “In
order
to
utilise
the
vast
amount
of
unlabelled
social
media
images,
an
unsupervised

approach
would
be
much
more
desirable,”
researchers
from
Arizona
State
University

wrote
in
their
paper.

• “As
of
2013,
87
millions
of
users
have
registered
with
Flickr.
Also,
it
was
estimated
that

about
20
billion
Instagram
photos
are
shared
to
2014.

• “To
our
best
knowledge,
USEA
is
the
first
unsupervised
sentiment
analysis
framework

for
social
media
images.”

• The
framework
infers
sentiments
by
combining
visual
data
with
accompanying
textual

data.
As
textual
data
is
often
incomplete
with
hardly
any
tags
or
noisy
with
irrelevant

comments,
relying
on
it
alone
is
difficult
when
doing
sentiment
analysis.

6

• Therefore,
the
researchers
used
the
supporting
textual
data
to
provide
semantic

information
on
the
images
to
enable
unsupervised
learning.

• “Textual
information
bridges
the
semantic
gap
between
visual
features
and
sentiment

labels.”

• The
researchers
crawled
images
from
Flickr
and
Instagram
users,
collecting
140,221

images
from
Flickr
and
131,224
from
Instagram.

• They
built
a
framework
to
classify
images
into
three
categories
or
class
labels
–
positive,

negative
and
neutral,
looking
at
image
captions
and
comments
associated
with
the

images.

• “Some
words
may
contain
sentiment
polarities.
For
example,
some
words
are
positive

such
as
‘happy’
and
‘terrific’;
while
others
are
negative
such
as
‘gloomy’
and

‘disappointed’.

•
“The
sentiment
polarities
of
words
can
be
obtained
via
some
public
sentiment
lexicons.

For
example,
the
sentiment
lexicon
MPQA
[Multiple
Perspective
Question
Answering]

contains
7,504
human
labeled
words
which
are
commonly
used
in
the
daily
life
with

2,721
positive
words
and
4,783
negative
words.

• “Second,
some
abbreviations
and
emoticons
are
strong
sentiment
indicators.
For

example,
‘lol’
[laugh
out
loud]
is
a
positive
indicator
while
‘:(‘
is
a
negative
indicator.”

• Visual
features
from
the
images
were
extracted
by
large-‐scale
visual
attribute
detectors,

with
term
frequency
and
stop
words
(removing
words
like
‘a’
and
‘the’)
used
to
form

text-‐based
features.

• The
framework
was
compared
to
other
sentiment
analysis
algorithms
such
as
Senti
API

for
unsupervised
sentiment
prediction
and
a
variant
of
the
framework,
USEA-‐T,
which

only
takes
textual
data
into
account
when
doing
sentiment
analysis.

• Other
methods
that
were
also
compared
with
the
USEA
framework
were
Sentibank
with

K-‐means
clustering,
which
uses
large
scale
visual
attribute
detectors,
and
adjective
and

nouns
visual
sentiment
description
pairs;
EL
with
K-‐means
clustering,
which
is
a
topical

graphical
model
for
sentiment
analysis;
and
Random,
which
randomly
guesses
to
predict

sentiment
labels
of
images.

• The
results
show
that
USEA
performed
better
than
all
the
other
algorithms
tested,

receiving
56.18
per
cent
accuracy
with
the
Flickr
dataset
compared
to
Senti
API
at
34.15

per
cent
and
USEA-‐T
at
40.22
per
cent.
With
the
Instagram
dataset,
it
received
59.94
per

cent
accuracy
compared
to
Senti
API
at
37.80
per
cent
and
USEA-‐T
at
36.41
per
cent.

• “The
proposed
framework
often
obtains
better
performance
than
baseline
methods.

There
are
two
major
reasons.
First,
textual
information
provides
semantic
meanings
and

sentiment
signals
for
images.
Second
we
combine
visual
and
textual
information
for

sentiment
analysis.”

• The
research
pointed
out
that
deep
learning
approaches
(many
hidden
layers
in
artificial

neural
networks)
to
this
have
shown
to
be
effective,
but
still
are
mostly
used
in
a

supervised
learning
way,
which
depends
on
the
availability
of
a
good
training
dataset

with
labels.

• “In
the
future,
we
will
exploit
more
social
media
sources,
such
as
link
information,
user

history,
geo-‐location,
etc.,
for
sentiment
analysis.”

• Source:
http://www.cio.com.au/article/580602/study-‐uncovers-‐unsupervised-‐
learning-‐framework-‐image-‐sentiment-‐analysis/?fp=16&fpid=1

There
are
some
opinions
from
Professor
Miller
about
text
mining

supervised
vs
unsupervised:

Unsupervised
text
analytics
problems
are
those
for
which
there
is
no
response
or

class
to
be
predicted.
Rather,
as
we
showed
with
the
movie
taglines,
the
task
is
to

identify
common
patterns
or
trends
in
the
data.
As
part
of
the
task,
we
may

define
text
measures
describing
the
documents
in
the
corpus.

For
supervised
text
analytics
problems
there
is
a
response
or
class
of
documents

to
be
predicted.
We
build
a
model
on
a
training
set
and
test
it
on
a
test
set.
Text

7

classification
problems
are
common.
Span
filtering
has
long
been
a
subject
of

interest
as
a
classification
problem,
and
many
e-‐mail
users
have
benefitted
from

the
efficient
algorithm
that
have
evolved
in
this
area.
In
the
context
of

information
retrieval,
search
engines
classify
documents
as
being
relevant
to
the

search
or
not.
Useful
modeling
techniques
for
text
classification
include
logistic

regression,
linear
discriminant
function
analysis,
classification
trees,
and
support

vector
machines.
Various
ensemble
or
committee
methods
may
be
employed.

Automatic
text
summarization
is
an
area
of
research
and
development
that
can

help
with
information
management.
Imagine
a
text
processing
program
with
the

ability
to
read
each
document
in
a
collection
and
summarize
it
in
a
sentence
or

two,
perhaps
quoting
from
the
document
itself.
Today’s
search
engines
are

providing
partial
analysis
of
documents
prior
to
their
being
displayed.
They

create
automated
summaries
for
fast
information
retrieval.
They
recognize

common
text
strings
associated
with
user
requests.
These
applications
of
text

analysis
comprise
tool
of
information
search
that
we
take
of
granted
as
part
of

our
daily
lives.

Seed
Program
(3)#
Data
Analysis
in
general
+
Bank
in
particular
(just
name
it

symphony
analysis)
>>>
unpublished
my
first
book
<data
analysis
is
a
symphony

in
big
data
jungle>

In
an
analysis
list
about
banking
data,
may
bring
my
version
listed*
live.
My
little

behemoth,
it’s
with
all
my
nurturing
from
learning.
Whereas
it’s
fully

understandable
my
deepest
respects
to
the
behemoths
who
has
been

authoritative
over
50
years
in
bank
data
analysis
relates,
especially
approached

with
seeable
continuous
advances
e.g.
machine
learning,
predictive
analytics.

.
Customer
portfolio
management

.
Customer
segmentation

.
RFM
models
&
Migration

.
Market
basket
analysis

.
Recommendation
tool

.
Existing
customer
analysis

.
Customer
acquisition

.
Customer
retention
including
churn
analysis,
and
the
side
of
risk
management

(over
50
years’
professions
in
FICO
&
Others)

.
Cross-‐selling
&
Up-‐selling

.
Multiple
channel
planning

.
ROI
modeler

.
Customer
lifetime
value
system

.
Techniques
in
predictive
analytics,
machine
learning
&
Neural
Network

.
Risk
analysis
(over
50
years’
professions
in
FICO
&
SAS
&
Others)

.
Fraud
analysis
(over
50
years’
professions
in
FICO
&
SAS
&
Others)

.
Credit
score
(over
50
years’
professions
in
FICO
&
Others)

(&
more
a
lot
about
financial
data
areas
that
probably
I
don’t
know,
related
to

over
50
years’
professions
in
FICO
&
SAS
&
Others)

.
Hypothesis
*
Experiments

8

• *about
the
list,

• It’s
suggested
to
remove
the
name
limitation
here
for
convenient
reading,

despite
name
system
in
list
is
simply
consistent
to
e-‐business.
My
learning

has
been
through
data
mining
methodology,
so
that,
within
my
available

data
capabilities,
it
enables
to
switch
verified
data
situation
with
specific

data
analysis
techniques
behind
the
name.
It
also
includes
some
instances

that
there
are
data
analysis
essences
I
have
learned
about
e.g.
logit,

conjunct
analysis
and
a
lot
etc.
despite
it
can’t
tell
from
the
listed
names.

And there are more small modelers related to modeling techniques.
Need to highlight Seed
Program
(4)#
the
innovation analytics would consist to
holistic analytics list and resonating to industry shift on both technology and bank
network including bank urbanization phenomena, why IoT is much relevant to bank
business, influences caused from millennials, mobile bank, e-wallet etc. This part will
be more involved into continuous industry insight decipher. The similar analytics
could be expanded into other sectors Seed
Program
(5)#, like retail, telecom,
travel/hotel, restaurant. Nonetheless, it’s still necessary to tackle the equation**.
From TENG:“Data is new currency. Banking it.” Roadmap as one of the topics [fig.4]
data in mock-up for category survey, for instance, Newer/Driver/Challenger.
My 1 case connects to bank analysis.
A few years ago, in my chat with my one ex-colleague, he talked about one hurdle
0"
1"
2"
3"
4"
5"
Newer" Driver" Challenger"
•  Data$Analy)cs$Matrix$consolidates$Bank$dynamic$insights$
(Newer/Driver/Challenger)$throughout$comprehensions$of$
shiBing$fric)on$which$is$caused$by$millennials’$dis)nc)on.$$
TENG"data"unme"FRAMEWROK"~"9th"pillar"
Industry"
Insight"
EGBusiness"
Consumer"
+/G"Channel"
Social"
Network"""
Brand"+/G"
Consumer"
Movie/
Drama"
IndexaPon"
""
Modeling$
Techniques$
Machine$
Learning/
Deep$
Learning$
Business$
Savvy$
Biz"
Model"
my$contacts:$$
erinteng$(at)$hotmail$(dot)$com$
139$1862$0956$

9

happened in performance marketing during his working in insurance services. In the
challenges to decide how much scaling is most appropriate during dealing with
targeting, there is a phenomena there is absolute loss of quality acquisitions in any
case when enlarging recruitment base.
Case Study, it’s all about logistic regression to fix targeting paradox.
All other things being equal, the customers with the highest predicted sales should be the ones the sales
team will approach first. Alternatively, we could set a cutoff for predicted sales. Customers above the
cutoff are the customers who get sales calls—these are the targets. Customers below the cutoff are not
given calls.
When evaluating a regression model using data from the previous year, we can determine how close
the predicted sales are to the actual/observed sales. We can find out the sum of the absolute values of
the residuals (observed minus predicted sales) or the sum of the squared residuals.
Another way to evaluate a regression model is to correlate the observed and predicted response values.
Or, better still, we can compute the squared correlation of the observed and predicted response values.
This last measure is called the coefficient of determination, and it shows the proportion of response
variance accounted for by the linear regression model. This is a number that varies between zero and
one, with one being perfect prediction.
If we plotted observed sales on the horizontal axis and predicted sales on the vertical axis, then the
higher the squared correlation between observed sales and predicted sales, the closer the points in the
plot will fall along a straight line. When the points fall along a straight line exactly, the squared
correlation is equal to one, and the regression model is providing a perfect prediction of sales, which is
to say that 100 percent of sales response is accounted for by the model. When we build a regression
model, we try to obtain a high value for the proportion of response variance accounted for. All other
things being equal, higher squared correlations are preferred.
The focus can be on predicting sales or on predicting cost of sales, cost of support, profitability, or
overall customer lifetime value. There are many possible regression models to use in with regression
methods.
To develop a classification model for targeting, we proceed in much the same way as with a regression,
except the response variable is now a category or class. For each customer, a logistic regression model,
for example, would provide a predicted probability of response. We employ a cut-off value for the
probability of response and classify responses accordingly. If the cut-off were set at 0.50, for example,
then we would target the customer if the predicted probability of response is greater than 0.50, and not
target otherwise. Or we could target all customers who have a predicted probability of response of
0.40, or 0.30, and so on. The value of the cut-off will vary from one problem to the next.
When observed binary responses or choices are about equally split between yes and no, for example,
we would use a cut-off probability of 0.50. That is, when the predicted probability of responding yes is
greater than 0.50, we predict yes. Otherwise, we predict no.
Logistic regression provides a means for estimating the probability of a favorable (yes) response to the
offer. The density lattice in figure 3.6 provides a pictorial representation of the model and a glimpse at
model performance.
To evaluate the performance of this targeting model, we look at a two-by-two contingency table or
confusion matrix showing the predicted and observed response values. A 50 percent cut-off does not
work in the Bank Marketing Study, given the low base rate of responses to the offer.
A 50 percent cut-off will not work for the bank, but using a 10 percent cutoff for the response variable
(accepting the term deposit offer or not), yields 65.9 percent accuracy in classification. The confusion
matrix for the logistic regression and 10 percent cut-off is shown.

10

The Bank Marketing Study is typical of target marketing problems. Response rates are low, much
lower than 0.50, so a 50 percent cut-off performs poorly. In fact, if bank analysts were to use a 50
percent cut-off, they would predict that every client would respond no, and the bank would target no
one. Too high a cut-off means the bank will miss out on many potential sales.
Too low a cut-off presents problems as well. Too low a cut-off means the bank will pursue sales with
large numbers of clients, many of whom will never subscribe to the term deposit offer. It is wise to
pick a cut-off that maximizes profit, given the unit revenues and costs associated with each cell of the
confusion matrix. Target marketing, employed in the right situations and with the right cut-offs, yields
higher profits for a company.
Source: <Modeling Techniques in Predictive Analytics>
Being through the enterprise & innovation equation**,
Business value = ecosystem x business model x category pacing x data skill x resources
End. Thank You.
My ‘thank you’ has to be sent to, with my rough counts, around data analysis gurus x1,000 ppl, and a
group of professions, authors, contributors x5,000 ppl, besides corporations, universities, institutions,
organizations, and other team members.
Mirroring infusive magnets, we can use data analysis capability to refine data into tangible data
model, thus it’s enable to decode human being’s new information adoption pattern when it
embeds into a shifting lifestyle movement. It’s ready to embark into a learning mode of the new
experiences. It’s found multiple dimensional relationships between customer and brand,
reciprocity since it’s along with disruptive technology revolution.

my model genuines.

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my model genuines.