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Related Experiment Video

Updated: Sep 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection.

Afek Ilay Adler1, Amichai Painsky1

  • 1The Industrial Engineering Department, Tel Aviv University, Tel Aviv 69978, Israel.

Entropy (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

Gradient Boosting Machines (GBM) with biased base learners show skewed feature importance. Using cross-validated unbiased learners improves GBM feature importance without sacrificing prediction accuracy.

Keywords:
classification and regression treesfeature importancegradient boostingtree-based methods

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Area of Science:

  • Machine Learning
  • Data Science
  • Statistical Modeling

Background:

  • Gradient Boosting Machines (GBM) are widely used for tabular data prediction.
  • Standard GBM implementations often use decision trees biased towards high-cardinality categorical variables.
  • This bias has been studied for predictive performance but not extensively for feature importance.

Purpose of the Study:

  • To investigate the impact of biased base learners on feature importance (FI) in Gradient Boosting Machines.
  • To propose and evaluate a method for mitigating this bias in GBM feature importance measures.

Main Methods:

  • Utilizing cross-validated (CV) unbiased base learners within the GBM framework.
  • Testing the proposed framework on both synthetic and real-world datasets.
  • Comparing feature importance measures and predictive performance against standard GBM implementations.

Main Results:

  • Biased base learners in GBM implementations lead to surprisingly biased feature importance, despite competitive predictive performance.
  • The proposed framework using CV unbiased base learners significantly improves all GBM feature importance measures.
  • Prediction accuracy is maintained at a comparable level to standard GBM implementations.

Conclusions:

  • The bias in base learners significantly affects GBM feature importance, not just predictive performance.
  • Employing cross-validated unbiased base learners is an effective and computationally efficient method to correct feature importance bias in GBM.
  • This approach enhances the reliability of feature importance analysis in Gradient Boosting Machines.