RATE Analytics
26 juni
Dennis Collaris
PhD Visualization
Fraud detection explanations
sick-leave insurances
PREVIOUS WORK
My solution
PREVIOUS WORK
Decision support
Diagnostics
Refinement
Justification
Applications
PREVIOUS WORK
Paper presented at:
Workshop on Human Interpretability in Machine Learning
Stockholm, Sweden
PREVIOUS WORK
DEBTOR MANAGEMENT
Effectiveness of debt collection strategies
Decision support
Diagnostics
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Design decisions
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Design decisions
Global
Local
DEBTOR MANAGEMENT
Design decisions
Surrogate learning
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Feature 1
Feature 2
Feature 3
Feature 1
Feature 2
Feature 3
Feature 1
Feature 2
Feature 3
DEBTOR MANAGEMENT
Help data scientists to create and tune explanatory surrogate models.
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Configuration view
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← Any tabular data set
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← Any Python classifier, or PMML
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← Different surrogate models
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Global overview
R²
Every line = an explanation
More information than traditional feature importance
Selecting subgroups
R²
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Local explanation view
← quality
← explanation
DEBTOR MANAGEMENT
Context view
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Context view
DEBTOR MANAGEMENT
Context view
Demo
Paper submitted to:
IEEE Visual Analytics Systems and Technology
Vancouver, Canada
Applicable to any machine learning model
LIME
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LIME
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LEMON
LIME
LEMON
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LIME / LEMON
Can be used for any Python model...
sklearn-pmml-model
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Can be used for any model...
Events
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NEXT STEPS
Opportunities
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Achmea
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Overlijdens Risico Verzekeringen (ORV) (Senna van Iersel)
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Health insurance churn prediction (Lizan Kops)
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Pricing GLMs (Joost van Bruggen)
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Recruitment analytics (Silke Lhoëst)
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Team Data Science (Schade Particulier) (Wouter Slot)
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Collaboration
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RATE colleagues
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Lorentz Grant Workshop @ Leiden
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IBM & Hardvard @ Boston
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NEXT STEPS
Topic
Global
Local