Poster
in
Workshop: 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
Feature Importance Measurement based on Decision Tree Sampling
CHAO HUANG · Diptesh Das · Koji Tsuda
Keywords: [ Trustworthy AI ] [ SAT ] [ Random Forest ] [ Decision tree ] [ reliability ] [ Interpretability ]
Random forest are effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed 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.