Feature Importance Measurement based on Decision Tree Sampling

Most of the existing methods are ad hoc, and do not have explicit Random forest is effective for prediction tasks control over the size and accuracy of a DT. For e.g., there are but the randomness of tree generation hinders interpretability greedy splitting-based (Quinlan, 1986; 2014; Breiman et al., in feature importance analysis. To 1984), Bayesian-based (Denison et al., 1998; Chipman et al., address this, we proposed DT-Sampler, a SATbased 1998; 2002; 2010; Letham et al., 2015), branch-and-bound method for measuring feature importance methods (Angelino et al., 2017) for DT construction.