  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.
