Barga Data Science lecture 9

Deriving Knowledge from Data at Scale

What is model overfitting?
How would you identify it?
How would you correct it?

What is model underfitting?
How would you identify it?
How would you correct it?

List methods for dealing with
missing values?
But what must you do first?

Describe stratification and
give an example of when
you would use it…

Why is feature selection
important?

Strategy to Incrementally Build on Foundation
• Select a Data Type
• Data Manipulation for Selected Data Type
• Feature Selection, Feature Creation, Transformation,…
• Machine Learning Algorithm
• Techniques and Association Tool(s)

You can make much stronger inferences about a woman named Brittany. That name was very popular from the mid-1980s
through the mid-1990s, but it wasn’t all that common before and hasn’t been since. If you know a Brittany, she is probably
of college age or just a bit older. Half of living American Brittanys are between the ages of 19 and 25

Optional Reading…

About the Experiment

Key Points

Read to Hack Your Machine Learning Development

Read to Hack Your Machine Learning Development
Cascade Classifier is one of the most popular face detection algorithms and the
default choice in OpenCV libraries as well. Highly accurate and very fast…
• 15 times faster than previous work at the time, fast enough for realtime;
• Intuition: Accurate and complex models are computational expensive. The
input are subjected to a series of increasing accurate and expensive models,
and the most expensive model will be used on only the most promising input.
So it asks a series of questions like: 1) Does model one thinks it is a face? If no,
stop. If Yes, ask model two. 2) Does model two thinks it is a face? If no, stop. If
yes, ask model three. And so on, until the last model also say yes.
• Feature-based then pixel-based. Haar-like features were created for the
images. Note working with pixels is generally very computational expensive.
• Haar-like features are basically high-level features over pixels. For example,
the feature observation that the region of the eyes is darker than the region of
the cheeks. This can be used as a feature input to the model.

Better, Best?
Simple (single) Classifier, Bagging, or Boosting?
Bagging: Multiple classifiers trained on different under-sampled subsets and
allow classifiers to vote on final decision, in contrast to using just one classifier.
Boosting: Series of classifiers to train on the dataset, but gradually putting more
emphasis on training examples that the previous classifiers have failed on, in the
hope of that the next classifier will focus on these harder examples. So in the
end, you will have a series of classifiers who are in general balanced but slightly
more focused on the hard training examples.
In practice, boosting beats bagging, either bagging and boosting will beat a
plain classifier. See ‘Bagging, Boosting and C4.5’ by JR Quinlan. Related work
and experience is that Random Forest models are as good as boosting.

Retrain on whole dataset after validating model?
Suppose you have a dataset split into 80% for training and 20% for validation,
do you follow ‘Plan A’ or ‘Plan B’?
Plan A
Plan B

Plan A

Plan A
hyper-parameters

Hyperparameter Tuning
Just to be sure we’re all on the same page…

Hyperparameter Tuning
Just to be sure we’re all on the same page…
Random search
• If the close-to-optimal region of hyperparameters occupies at least 5%
of the grid surface, then random search with 60 trials will find that region
with high probability.
With its utter simplicity and surprisingly reasonable performance,
random search is my to-go method for hyperparameter tuning. It’s
trivially parallelizable, just like grid search, but it takes much fewer
tries and performance almost as well most of the time

Feature Creation

Process of Feature Engineering

Why?
most important

• Google Refine
• Weka
• Brainstorming

https://www.youtube.com/watch?v=yNccGtn3Wb0
https://www.youtube.com/watch?v=cO8NVCs_Ba0
https://www.youtube.com/watch?v=5tsyz3ibYzk

Not all errors are equal
We need better metrics…

True
Label
Predicted Label
Confusion
matrix

True Positive Rate True
Negative Rate False Positive Rate False Negative Rate

average per-class accuracy
average of the accuracy for each class

Frequently, one might look at only the top k items from the ranker, k = 5, 10,
20, 100, etc. Then the metrics would be called “precision@k” and “recall@k.”

Frequently, one might look at only the top k items from the ranker, k = 5, 10,
20, 100, etc. Then the metrics would be called “precision@k” and “recall@k.”
One might average precision and recall scores for each query and look at
“average precision@k” and “average recall@k.” (Analogous to the relationship
between accuracy and average per-class accuracy for classification.)

Once you can compute precision and recall, you are often able to produce
precision/recall curves. Suppose that you are attempting to identify spam. You
run a learning algorithm to make predictions on a test set. But instead of just
taking a “yes/no” answer, you allow your algorithm to produce its confidence.
For instance, using a perceptron, you might use the distance from the
hyperplane as a confidence measure. You can then sort all of your test emails
according to this ranking. You may put the most spam-like emails at the top
and the least spam-like emails at the bottom

Once you can compute precision and recall, you are often able to produce precision/recall curves. Suppose
that you are attempting to identify spam. You run a learning algorithm to make predictions on a test set. But
instead of just taking a “yes/no” answer, you allow your algorithm to produce its confidence. For instance, using
a perceptron, you might use the distance from the hyperplane as a confidence measure. You can then sort all
of your test emails according to this ranking. You may put the most spam-like emails at the top and the least
spam-like emails at the bottom
Once you have this sorted list, you can choose how aggressively you want your
spam filter to be by setting a threshold anywhere on this list. One would hope
that if you set the threshold very high, you are likely to have high precision (but
low recall). If you set the threshold very low, you’ll have high recall (but low
precision). By considering every possible place you could put this threshold,
you can trace out a curve of precision/recall values. This allows us to ask the
question: for some fixed precision, what sort of recall can I get…

• Method 1
• Method 2

Method 1
Method 2

Method 1
Method 2
very small
differences quite a
large difference

Sometimes we want a single number that informs us of the quality of the
solution. A popular way to combe precision and recall into a single number is
by taking their harmonic mean. This is known as the balanced f-measure:
The reason to use a harmonic mean rather than an arithmetic mean is that it
favors systems that achieve roughly equal precision and recall. In the extreme
case where P = R, then F = P = R. But in the imbalanced case, for instance P =
0.1 and R = 0.9, the overall f-measure is a modest 0.18.

NDCG
treat all retrieved items equally

NDCG

Regression Metrics

This was not an exhaustive list or coverage, but
I hope the next time you encounter a new
evaluation metric you can deconstruct it, identify
what is it measuring and why – to align with the
business objective or function of the model?

10 minutes break…

X
Y
N1
N2
o1
o2
O3

Related problems
* N. Talleb, The Black Swan: The Impact of the Highly Probable?, 2007

Tid SrcIP
Start
time
Dest IP Dest
Port
Number
of bytes
Attack
1 206.135.38.95 11:07:20 160.94.179.223 139 192 No
2 206.163.37.95 11:13:56 160.94.179.219 139 195 No
3 206.163.37.95 11:14:29 160.94.179.217 139 180 No
4 206.163.37.95 11:14:30 160.94.179.255 139 199 No
5 206.163.37.95 11:14:32 160.94.179.254 139 19 Yes
6 206.163.37.95 11:14:35 160.94.179.253 139 177 No
7 206.163.37.95 11:14:36 160.94.179.252 139 172 No
8 206.163.37.95 11:14:38 160.94.179.251 139 285 Yes
9 206.163.37.95 11:14:41 160.94.179.250 139 195 No
10 206.163.37.95 11:14:44 160.94.179.249 139 163 Yes
10

Tid SrcIP Duration Dest IP
Number
of bytes
Internal
1 206.163.37.81 0.10 160.94.179.208 150 No
2 206.163.37.99 0.27 160.94.179.235 208 No
3 160.94.123.45 1.23 160.94.179.221 195 Yes
4 206.163.37.37 112.03 160.94.179.253 199 No
5 206.163.37.41 0.32 160.94.179.244 181 No

Normal
Anomaly

Anomalous Subsequence

Evaluation of Anomaly Detection – F-value

Evaluation of Outlier Detection – ROC & AUC
Standard measures for evaluating anomaly detection problems:
Predicted
class
Confusion
matrix
NC C
NC TN FPActual
class C FN TP
anomaly class – C
normal class – NC

Using Support Vector Machines
SVM Classifiers
• Main idea
• Normal data records belong to high density data regions
• Anomalies belong to low density data regions
• Use unsupervised approach to learn high density and low density data
regions
• Use SVM to classify data density level
• Main idea
• Data records are labelled (normal network behaviour vs. intrusive)
• Use standard SVM for classification

Using Replicator Neural Networks
Target
variables
Input

Using Support Vector Machines
origin push the hyper plane
away from origin as
much as possible

Nearest Neighbour Based Techniques

Nearest Neighbor Based Techniques

Distance based Outlier Detection
Nearest Neighbour (NN) approach
• For each data point d compute the distance to the k-th nearest neighbour dk
• Sort all data points according to the distance dk
• Outliers are points that have the largest distance dk and therefore are located
in the more sparse neighbourhoods
• Usually data points that have top n% distance dk are identified as outliers
• n – user parameter

Model-based approaches to anomaly detection construct a profile of normal
instances, then identify instances that do not conform to this profile as anomalies.
Isolation Forrest is a fundamentally different model-based method that explicitly
isolates anomalies instead of profiles normal points. iForests create an algorithm
which has a linear time complexity with a low constant and a low memory
requirement.
Empirical evaluations shows that iForest performs favorably to OCSVM a near-
linear time complexity distance-based method, NN and random forests in terms
of AUC and processing time, and especially in large data sets. iForest also works
well in high dimensional problems which have a large number of irrelevant
attributes, and in situations where training set does not contain any anomalies.

Randomly select a dimension (feature) and then
randomly select a cut point within that feature
space, recurse, build a tree until leaf node has
only n (for example 1).
Create an ensemble of such trees, merge the
results.
Outliers do not have neighbors (mass) and will
be close to the root (top) of the tree…

“Isolation Forest”
• When building a tree, anomalies are likely to be
isolated closer to the root of the tree; whereas
normal points appear deeper in the tree structure
• No need to profile normal data points
• No distance or density measures
• Gap
• Lacks explanatory power

Neural Networks
http://www.youtube.com/watch?v=GrugzF0-V3I

X1 X2 X3 Y
1 0 0 0
1 0 1 1
1 1 0 1
1 1 1 1
0 0 1 0
0 1 0 0
0 1 1 1
0 0 0 0
X1
X2
X3
Y
Black box
Output
Input
Output Y is 1 if at least two of the three inputs are equal to 1.

X1 X2 X3 Y
1 0 0 0
1 0 1 1
1 1 0 1
1 1 1 1
0 0 1 0
0 1 0 0
0 1 1 1
0 0 0 0

X1
X2
X3
Y
Black box
0.3
0.3
0.3 t=0.4
Output
node
Input
nodes





otherwise0
trueisif1
)(where
)04.03.03.03.0( 321
z
zI
XXXIY


X1
X2
X3
Y
Black box
w1
t
Output
node
Input
nodes
w2
w3
)( tXwIY
i
ii  
Perceptron Model
)( tXwsignY
i
ii  
or

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+ −
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Linearly separable data: Non-linearly separable data:
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Activation
function
g(Si )
Si
Oi
I1
I2
I3
wi1
wi2
wi3
Oi
Neuron iInput Output
threshold, t
Input
Layer
Hidden
Layer
Output
Layer
x1 x2 x3 x4 x5
y
Training ANN means learning the weights of
the neurons

• Inputs
input layer
hidden layer
output layer
feed-forward
nonlinear regression

input layer # of hidden
layers each hidden layer output layer
• Normalize
input
• Output
unacceptable
different network topology different set of
initial weights

𝛻𝐸 𝒘 =
𝜕𝐸
𝜕𝑤0
,
𝜕𝐸
𝜕𝑤1
, … ,
𝜕𝐸
𝜕𝑤 𝑑

114
 2
),( 
i
iii XwfYE

Backpropagation
Iteratively process a set of training tuples & compare the network's
prediction with the actual known target value
minimize the mean squared error
backwards
backpropagation

Stochastic Gradient Descent

• Cross-validation
0E
0E

WEKA provides user control of training
parameters:
• # of hidden layers
• Learning rate
• # of iterations or epochs (“training time”)
• Increment of weight adjustments in back
propogation (“learning rate”)
• Controls on varying changes to increments
(“momentum”) and weight decay

Multilayered Neural Networks require a lot
of experimentation to get right…
a lot…
I’m serious…

Hands On

Are they any good?

rare

That’s all for tonight….

Barga Data Science lecture 9

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Barga Data Science lecture 9