Feature Importance Measurement based on Decision Tree Sampling
This work addresses interpretability issues in feature importance analysis for users of tree-based models, but it appears incremental as it builds on existing random forest methods.
The authors tackled the problem of interpretability in feature importance analysis for tree-based models by proposing DT-Sampler, a SAT-based method that reduces parameters and improves interpretability and stability compared to random forests.
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.