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GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification
[article]
2017
arXiv
pre-print
We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance. To address this problem, we present GapTV, an approach that is conceptually related both to CART and to the more recent CRISP algorithm, a state-of-the-art alternative method for interpretable nonlinear regression. GapTV divides the
arXiv:1702.07405v1
fatcat:6utxds4fqrdvrdd32efz6xxxza