# Trade-offs in Explanatory Model Learning  - PowerPoint PPT Presentation

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1. 22 nd of February 2012. Trade-offs in Explanatory Model Learning . DCAP Meeting Madalina Fiterau. 2. Outline. Motivation: need for interpretable models Overview of data analysis tools Model evaluation – accuracy vs complexity Model evaluation – understandability

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#### Presentation Transcript

1

22nd of February 2012

## Trade-offs in Explanatory Model Learning

DCAP Meeting

2

### Outline

• Motivation: need for interpretable models

• Overview of data analysis tools

• Model evaluation – accuracy vs complexity

• Model evaluation – understandability

• Example applications

• Summary

Border control: vehicles are scanned

Human in the loop interpreting results

prediction

feedback

vehicle scan

4

### Boosted Decision Stumps

• Accurate, but hard to interpret

How is the prediction derived from the input?

5

### Decision Tree – More Interpretable

yes

no

no

yes

Consider balance of Th232, Ra226 and Co60

yes

no

Uranium level > max. admissible for ceramics

Threat

Clear

6

### Motivation

Many users are willing to trade accuracy to

better understand the system-yielded results

Need: simple, interpretable model

Need: explanatory prediction process

7

8

9

2 Gaussians

Uniform cube

(X,Y) plot

### EOP Execution Example – 3D data

Step 1: Select a projection - (X1,X2)

EOP Execution Example – 3D data

Step 1: Select a projection - (X1,X2)

### EOP Execution Example – 3D data

h1

Step 2: Choose a good classifier - call it h1

### EOP Execution Example – 3D data

Step 2: Choose a good classifier - call it h1

### EOP Execution Example – 3D data

OK

NOT OK

Step 3: Estimate accuracy of h1 at each point

### EOP Execution Example – 3D data

Step 3: Estimate accuracy of h1 for each point

### EOP Execution Example – 3D data

Step 4: Identify high accuracy regions

### EOP Execution Example – 3D data

Step 4: Identify high accuracy regions

### EOP Execution Example – 3D data

Step 5:Training points - removed from consideration

### EOP Execution Example – 3D data

Step 5:Training points - removed from consideration

### EOP Execution Example – 3D data

Finished first iteration

### EOP Execution Example – 3D data

Finished second iteration

### EOP Execution Example – 3D data

Iterate until all data is accounted for

or error cannot be decreased

[x1x2] in R1 ?

yes

h1(x1x2)

no

yes

[x2x3] in R2 ?

h2(x2x3)

no

yes

[x1x3] in R3 ?

h3(x1x3)

no

Default Value

### Parametric / Nonparametric Regions

Query point

decision

decision

Correctly classified

Incorrectly classified

n3

n2

p

n5

n1

n4

25

### EOP in context

Similarities

Differences

Default classifiers in leafs

CART

Decision structure

Models trained on all features

Feating

Local models

Subspacing

Low-d projection

Keeps all data points

Multiboosting

Ensemble learner

Committee decision

Boosting

26

### Outline

• Motivation: need for interpretable models

• Overview of data analysis tools

• Model evaluation – accuracy vs complexity

• Model evaluation – understandability

• Example applications

• Summary

27

• Real valued features, binary output

• Artificial data – 10 features

• Low-d Gaussians/uniform cubes

• UCI repository

• Application-related datasets

• Results by k-fold cross validation

• Accuracy

• Complexity = expected number of vector operations performed for a classification task

• Understandability = w.r.t. acknowledged metrics

### Overview of datasets

EOP is often less accurate, but not significantly

the reduction of complexity is statistically significant

### EOP vsAdaBoost - SVM base classifiers

Accuracy

Complexity

Boosting

EOP (nonparametric)

mean diff in accuracy: 0.5%

mean diff in complexity: 85

p-value of paired t-test: 0.832

p-value of paired t-test: 0.003

### EOP (stumps as base classifiers) vs CART on data from the UCI repository

EOP P.

Accuracy

Complexity

• Parametric EOP yields the simplest models

• CART is the most accurate

CART

EOP N.

• Why are EOP models less complex?

Typical XOR dataset

• Why are EOP models less complex?

• CART

• is accurate

• takes many iterations

• does not uncover or leverage structure of data

Typical XOR dataset

Why are EOP models less complex?

• CART

• is accurate

• takes many iterations

• does not uncover or leverage structure of data

Typical XOR dataset

• EOP

• equally accurate

• uncovers structure

+

o

Iteration 1

o

+

Iteration 2

At low complexities, EOP is typically more accurate

Error Variation With Model Complexity for EOP and CART

EOP_Error-CART_Error

Depth of decision tree/list

35

### UCI data – Accuracy

White box models – including EOP – do not lose much

36

### UCI data – Model complexity

White box models – especially EOP – are less complex

Complexity of Random Forests is huge

- thousands of nodes -

• Accuracy-targeting EOP

• Identifies which portions of the data can be confidently classified with a given rate.

• Allowed to set aside the noisy part of the data.

### Robustness

Accuracy of AT-EOP on 3D data

Accuracy

Max allowed expected error

38

### Outline

• Motivation: need for interpretable models

• Overview of data analysis tools

• Model evaluation – accuracy vs complexity

• Model evaluation – understandability

• Example applications

• Summary

### Metrics of Explainability*

* L.Geng and H. J. Hamilton - ‘Interestingness measures for data mining: A survey’

For 3 out of 4 metrics, EOP beats CART

### Evaluation with usefulness metrics

BF =Bayes Factor. L = Lift. J = J-score. NMI = Normalized Mutual Info

• Higher values are better

41

### Outline

• Motivation: need for interpretable models

• Overview of data analysis tools

• Model evaluation – accuracy vs complexity

• Model evaluation – understandability

• Example applications

• Summary

42

### Spam Detection (UCI ‘SPAMBASE’)

• 10 features: frequencies of misc. words in e-mails

• Output: spam or not

Complexity

43

• classifier labels everything as spam

• high confidence regions do enclose mostly spam and:

• Incidence of the word ‘your’ is low

• Length of text in capital letters is high

44

### Spam Detection – Iteration 2

• the required incidence of capitals is increased

• the square region on the left also encloses examples that will be marked as `not spam'

45

### Spam Detection – Iteration 3

• Classifier marks everything as spam

• Frequency of ‘our’ and ‘hi’ determine the regions

word_frequency_hi

### Example Applications

Stem Cells

ICU Medication

identify combinations of drugs that correlate with readmissions

explain relationships between treatment

and cell evolution

sparse features

class imbalance

Nuclear Threats

Fuel Consumption

identify causes of excess fuel consumption among driving behaviors

support interpretation of radioactivity detected in cargo containers

- high-d data

- train/test from different distributions

47

### Effects of Cell Treatment

• Monitored population of cells

• 7 features: cycle time, area, perimeter ...

• Task: determine which cells were treated

Complexity

48

49

### Mimic Medication Data

• Features: dosage for each drug

Complexity

50

51

### Predicting Fuel Consumption

• 10 features: vehicle and driving style characteristics

• Output: fuel consumption level (high/low)

Complexity

52

53

### Nuclear threat detection data

325 Features

• Random Forests accuracy: 0.94

• Rectangular EOP accuracy: 0.881

… but

Regions found in 1st iteration for Fold 0:

• incident.riidFeatures.SNR [2.90,9.2]

Regions found in 1stiteration for Fold 1:

• incident.rpmFeatures.gamma.sigma [2.5, 17.381]

• incident.rpmFeatures.gammaStatistics.skewdose [1.31,…]

No match

55

### Summary

• In most cases EOP:

• maintains accuracy

• reduces complexity

• identifies useful aspects of the data

• EOP typically wins in terms of expressiveness

• Open questions:

• What if no good low-dimensional projections exist?

• What to do with inconsistent models in folds of CV?

• What is the best metric of explainability?