Feature Importance Measurement based on Decision Tree Sampling
Chao Huang Diptesh Das Koji Tsuda
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.