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Reluctant Generalised Additive Modelling.

J Kenneth Tay1, Robert Tibshirani1,2

  • 1Department of Statistics, Stanford University, Stanford, California, USA.

International Statistical Review = Revue Internationale De Statistique
|September 5, 2022
PubMed
Summary
This summary is machine-generated.

Reluctant generalised additive modelling (RGAM) introduces a scalable algorithm for fitting sparse GAMs. This method prioritizes linear features over non-linear ones, enhancing model accuracy for various data types.

Keywords:
Feature selectiongeneralised additive modelshigh-dimensionalnon-linearregressionsparsity

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Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Sparse generalised additive models (GAMs) extend linear models for non-linear relationships.
  • Accurate modelling is crucial when linearity is a poor assumption.
  • Existing methods for sparse GAMs face scalability challenges.

Purpose of the Study:

  • To propose a scalable multi-stage algorithm for fitting sparse GAMs.
  • To introduce the reluctant generalised additive modelling (RGAM) approach.
  • To extend sparse GAM fitting to diverse data types.

Main Methods:

  • Developed a multi-stage algorithm named reluctant generalised additive modelling (RGAM).
  • RGAM prioritizes linear features over non-linear ones when model performance is comparable.
  • The RGAM algorithm is designed for fitting sparse GAMs at scale.

Main Results:

  • RGAM effectively fits sparse generalised additive models at scale.
  • The method demonstrates flexibility, extending to binary, count, and survival data.
  • Effectiveness validated through real and simulated datasets.

Conclusions:

  • RGAM offers an efficient and scalable solution for fitting sparse GAMs.
  • The algorithm's principle of preferring linear features aids model interpretability and accuracy.
  • RGAM provides a versatile tool for advanced statistical modelling across various data distributions.