Convex Regression with Interpretable Sharp Partitions
Ashley Petersen, Noah Simon, Daniela Witten; 17(94):1−31, 2016.
We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data- adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low- variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.
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