Lightning talk from PyData Berlin 2018

1. The Five Tribes of ML Explainers
(and what you can learn from each)
Michał Łopuszyński
PyData Berlin, 07.07.2018

2. Intro
Looking at performance metrics for your model is great but....

3. Intro
Looking at performance metrics for your model is great but....
Why?
Why?
Why?
Why?
Why?
Why? your model, predicts what it predicts?

4. The Featurists

5. The Featurists
Feature importance•
Feature importance from models (RF, ExtraTrees, Boosted trees, linear models)•
Model agnostic feature importance (e.g. permutation importance from ELI5)•
Feature selection•
Filters - e.g. filtering most correlated features•
Wrappers - e.g. forward/backward selection•
Embedded methods - e.g. lasso•
Idea: Find features important to your model•
all my features
Model Class Reliance: Variable Importance Measures
for any Machine Learning Model Class, from the
“Rashomon” Perspective (2018)

6. The Speculators

7. The Speculators
Idea: Check how your model responds to change of one variable•
Answer 1: Partial dependence plots:•
Example from
free book
Interpretable ML
by Christoph Molnar

8. The Speculators
Idea: Check how your model responds to change of one variable•
Answer 2: Individual Conditional Expectations•
Example from
free book
Interpretable ML
by Christoph Molnar

9. The Localizers

10. The Localizers
Idea: Fit interpretable model, which is locally correct•
Simple model = Linear
Why Should I Trust You?
Explaining the Predictions of Any Classifier
Ribeiro, Singh, Guestrin
LIME
Simple model = Rules
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, Singh, Guestrin
Anchors

11. The Convoluters

12. The Convoluters (only for ConvNets)
Two Ideas: Visualize the important regions in the image•
Example: Labrador
One of the important
high level features for
Labradors
+ high level features
"Labradorish" parts of image
Explanation
Figures from The Building Blocks of Interpretability, Olah et al, distill.pub

13. The Trainalyzers

14. The Trainalyzers
Idea: Which training examples contributed mostly to a given prediction•
Understanding Black-box Predictions
via Influence Functions,
W. Koh, P. Liang
Sample approach - influence functions

15. I collect links to interesting papers & soft
@lopusz
github.com/lopusz/awesome-interpretable-machine-learning
There is a lot more!