crisp - Fits a Model that Partitions the Covariate Space into Blocks in
a Data- Adaptive Way
Implements convex regression with interpretable sharp
partitions (CRISP), which considers the problem of predicting
an outcome variable on the basis of two covariates, using an
interpretable yet non-additive model. 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.
More details are provided in Petersen, A., Simon, N., and
Witten, D. (2016). Convex Regression with Interpretable Sharp
Partitions. Journal of Machine Learning Research, 17(94): 1-31
<http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
