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  "Description": "Implements convex regression with interpretable sharp\npartitions (CRISP), which considers the problem of predicting\nan outcome variable on the basis of two covariates, using an\ninterpretable yet non-additive model. CRISP partitions the\ncovariate space into blocks in a data-adaptive way, and fits a\nmean model within each block. Unlike other partitioning\nmethods, CRISP is fit using a non-greedy approach by solving a\nconvex optimization problem, resulting in low-variance fits.\nMore details are provided in Petersen, A., Simon, N., and\nWitten, D. (2016). Convex Regression with Interpretable Sharp\nPartitions. Journal of Machine Learning Research, 17(94): 1-31\n<http://jmlr.org/papers/volume17/15-344/15-344.pdf>.",
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