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arXiv · 2307.13333

Feature Importance Measurement based on Decision Tree Sampling

Chao Huang·Diptesh Das·Koji Tsuda
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Abstract

Random forest is effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed DT-Sampler, a SAT-based method for measuring feature importance in tree-based model. Our method has fewer parameters than random forest and provides higher interpretability and stability for the analysis in real-world problems. An implementation of DT-Sampler is available at https://github.com/tsudalab/DT-sampler.

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Chao Huang, Diptesh Das, Koji Tsuda. 2023-07-25. Feature Importance Measurement based on Decision Tree Sampling. https://arxiv.org/abs/2307.13333

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