Teamoverleg
May 7th
Dennis Collaris
PhD Visualization
Predictive model interpretability
MY RESEARCH
Black box model
75% risk!
Black box
model
Domain expert
Explanation
Aha!
But why?
Data
Explainer
Topics
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Random Forest explanations (ziekteverzuim)
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LIME & LEMON
-
ExplainExplore (debiteurenmanagement)
Explanations for
fraud detection
GRADUATION
Real world scenario
Data
- Lots of missing values
Model
- 100 Random Forest
- 500 trees each
- ~25 decisions per tree
- 1.312.471 models total!
×
OOB error: 27.7%
My solution
Feature contribution
[1] Palczewska, Anna et. al. Interpreting random forest classification models using a feature contribution method. In Integration of reusable systems, pp. 193–218. Springer, 2014.
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7 : 7
6 : 2
...
\(Y_{mean}\) = 0.5
\(Y_{mean}\) = 0.75
\(LI_{X}\) = 0.25
Contribution per Decision Tree:
\(FC_{i,t}^f = \sum_{N \in R_{i,t}} LI_f^N\)
Contribution per Random Forest:
\(FC_i^f = \frac{1}{T}\sum_{t=1}^T FC_{i,t}^f\)
X < 2.5
Partial dependence
[2] Friedman, Jerome H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5): pp. 1189–1232, 2001.
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Duration illness
Fraud?
Fraud (55%)
Non-fraud (35%)
| Company | ABC Inc |
| Employees | 5 |
| Duration illness | days |
| ... | ... |
Fraud (65%)
Fraud (90%)
Non-fraud (45%)
Non-fraud (40%)
Non-fraud (25%)
Local rule extraction
[3] Ribeiro, Marco Tulio et. al. Why should i trust you?: Explaining the predictions of any classifier. In
Proceedings of the 22nd ACM SIGKDD, pp. 1135–1144. ACM, 2016.
[4] Deng, Houtao. Interpreting tree ensembles with inTrees. arXiv preprint arXiv:1408.5456 , pp. 1–18, 2014.
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Fraud team happy! 🎉
Paper accepted for:
Workshop on Human Interpretability in Machine Learning
@
Beschikbaar bij
DEC team (Leon Vink)
LIME
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-2 -1 0 1 2 d
LEMON
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Beschikbaar:
- LIME github.com/marcotcr/lime
- LEMON binnenkort!
EXPLAIN-EXPLORE
- What is a good region of interest?
- What is a good explanation?
Video
Beschikbaar:
Indien accepted: eind oktober!
Benefits
-
Data scientist
Conceptual framework for evaluating explanation techniques.
-
Decision maker / Regulator / User
Can explore explanations most relevant to his task.
What factors play a role?
- Simple, accurate and generic model does not exist.
- Any explanation is a simplified model.
-
There is a difference between desiderata about the model and explanation.
- ML explanations always follow the "common effect" causal pattern (Keil, 2006).
Assumptions
What factors play a role?
Word cloud
What factors play a role?
Spreadsheet
What factors play a role?
High-level interaction
Next steps...
- Explore sub categories of high-level desiderata
(+ relations)
- Quantifiable metrics to approximate these desiderata.
- Paper?
Fraud detection model
75% Fraud
Fraud
Detection
Model
Fraud team
| Company | ABC Inc |
| Employees | 5 |
| Illness duration | 14 days |
| Premium rate | 5% |
| ... | ... |
Insurance policy
Explanation
Aha!
But why?
Global vs Local
In general: Duration of illness is important
For this employer: Report date of sickness is important
Global vs Local
Solution
Deck May 7th
By iamdecode
