https://github.com/jphall663/awesome-machine-learning-interpretability
https://christophm.github.io/interpretable-ml-book/index.html
Global Interpretability
- Partial Dependence and Partial Dependence Plot (PDP)
- Individual Conditional Expectation (ICE)
- Total and two-way H Statistics
- Global Feature importance using permutation
- Global surrogate model
Local Interpretability
- Local Interpretable Model-agnostic Explanations (LIME)
- Shapley additive explanation
An intuitive way to understand the Shapley value is the following illustration: The feature values enter a room in random order. All feature values in the room participate in the game (= contribute to the prediction). The Shapley value of a feature value is the average change in the prediction that the coalition already in the room receives when the feature value joins them.
The interpretation of the Shapley value is: Given the current set of feature values, the contribution of a feature value to the difference between the actual prediction and the mean prediction is the estimated Shapley value.
Fairness
- Independence: looks at the distribution of the scores.
- Demographic Parity
- Separation: looks at different performance metrics and ROC curves.
- Equality of Opportunity, Equalized odds, Positive Predictive Parity, Negative Predictive Parity, Accuracy Parity, Predictive Value Parity
- Sufficiency: looks at the calibration curves (fraction of positive instances among set of all instances assigned a score value).
Performance