Convex Regression with Interpretable Sharp Partitions

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. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

Authors

• Ashley Petersen
• Noah Simon
• Daniela Witten

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