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Explainable Machine learning Exercises

Dataset

Bike dataset to predict the number of bikes in a particular day (regression problem)
Cervical Cancer Dataset to predict Biopsy (classification problem)

Methods and Exploration, What every exercise is about?

CE1-

Random Forest regressor, Adjusted R2 Score
Generation of effect plot of the Lasso algorithm
Interaction feature and Linear Regression assumptions

CE2-

Logistic Regression, Odds ratio
Decision Trees, Feature importance from Mean Decrease in Impurity
RuleFit - Extract rules using CART and adding those rules as features then using LASSO
Extract highest support attribute from rules

CE3-

Partial Dependence Plots
Individual Conditional Expectation plots
M-plot
Accumulated local effects
Permutation feature importance

CE4 -

Local post-hoc explanations
1. Counterfactual explanations
2. Local interpretable model-agnostic explanations
3. Shapley Additive explanations, Kernel SHAP

[2023]