Feature Importance Measurement based on Decision Tree Sampling
Machine Learning
2023-07-26 v1
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.
Cite
@article{arxiv.2307.13333,
title = {Feature Importance Measurement based on Decision Tree Sampling},
author = {Chao Huang and Diptesh Das and Koji Tsuda},
journal= {arXiv preprint arXiv:2307.13333},
year = {2023}
}