Feature Importance

Interpreting Random Forests

A deep dive into techniques for interpreting random forest models — going beyond accuracy to understand what the model has learned. Particularly useful because random forests were the dominant ensemble method before gradient boosting took over, and interpretability was the main complaint against them.