Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection

Entropy

Amichai Painsky¹  Afek Ilay Adler¹ 

[1] The Industrial Engineering Department, Tel Aviv University, Tel Aviv 69978, Israel;

关键词: gradient boosting;    feature importance;    tree-based methods;    classification and regression trees;   

DOI  :  10.3390/e24050687

来源: DOAJ

【 摘 要 】

Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically, most implementations utilize decision trees that are typically biased towards categorical variables with large cardinalities. The effect of this bias was extensively studied over the years, mostly in terms of predictive performance. In this work, we extend the scope and study the effect of biased base learners on GBM feature importance (FI) measures. We demonstrate that although these implementation demonstrate highly competitive predictive performance, they still, surprisingly, suffer from bias in FI. By utilizing cross-validated (CV) unbiased base learners, we fix this flaw at a relatively low computational cost. We demonstrate the suggested framework in a variety of synthetic and real-world setups, showing a significant improvement in all GBM FI measures while maintaining relatively the same level of prediction accuracy.

【 授权许可】

Unknown