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Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing

Wesley Tansey,Jesse Thomason,James G. Scott

Published 2017 in arXiv: Machine Learning

ABSTRACT

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 Maximum Variance Total Variation denoising (MVTV), 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. MVTV divides the feature space into blocks of constant value and fits the value of all blocks jointly via a convex optimization routine. Our method is fully data-adaptive, in that it incorporates highly robust routines for tuning all hyperparameters automatically. We compare our approach against CART and CRISP via both a complexity-accuracy tradeoff metric and a human study, demonstrating that that MVTV is a more powerful and interpretable method.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv: Machine Learning

  • Publication date

    2017-06-16

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1708.01947
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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