Unbiased Measurement of Feature Importance in Tree-Based Methods

Zhou, Zhengze, Hooker, Giles

arXiv.org Machine Learning 

This paper examines split-improvement feature importance scores for tree-based methods. Starting with Classification and Regression Trees (CART; Breiman, 2017) and C4.5 (Quinlan, 2014), decision trees have been a workhorse of general machine learning, particularly within ensemble methods such as Random Forests (RF; Breiman, 2001) and Gradient Boosting Trees (Friedman, 2001). They enjoy the benefits of computational speed, few tuning parameters and natural ways of handling missing values.