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Interpretability in Generalized Additive Models

In: Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

Author

Listed:
  • S. N. Wood

    (School of Mathematics, University of Edinburgh)

  • Y. Goude

    (EDF R&D)

  • M. Fasiolo

    (School of Mathematics, University of Bristol)

Abstract

Modelling the effect of a covariate vector on the distribution of a response variable, requires some structural assumptions, if the curse of dimensionality is to be avoided. Generalized additive models (GAMs) assume that the effects of the covariates are additive, with no, or only low order, interactions between effects. The additive assumption ensures scalability in the number of covariates and facilitates computational efficiency during model fitting. It also enhances model interpretability, which is critically important during model building and checking, as well as for communicating modelling results. This chapter formally introduces standard GAMs, as well as more flexibile GAMs for location shape and scale (GAMLSS). Is also shows how to interactively build and improve GAM and GAMLSS models via the mgcv and mgcViz R packages, which exploit their modular and interpretable structure. The final part of the chapter shows how to exploit the additive structure of GAMs to build powerful predictive models, by using random forests and online aggregation methods.

Suggested Citation

  • S. N. Wood & Y. Goude & M. Fasiolo, 2022. "Interpretability in Generalized Additive Models," Springer Books, in: Antonio Lepore & Biagio Palumbo & Jean-Michel Poggi (ed.), Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches, chapter 0, pages 85-123, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-12402-0_4
    DOI: 10.1007/978-3-031-12402-0_4
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