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Model Fitting With Tree-Structured Regression

In: Computing Science and Statistics

Author

Listed:
  • Min-Ching Huang

    (SPSS Inc.)

  • Wei-Yin Loh

    (University of Wisconsin-Madison)

Abstract

This article studies tree-structured regression using piece-wise linear fits. Most of the tree regression methods search over essentially all possible partitions to find the best split, with the goodness of a split measured by how much it decreases the impurity of the subsamples. We propose a new method in that each piece is obtained by recursive partitioning of sample space, using analysis of the residuals from a linear model fitted to each partition to select the cut-plane, and cross-validation estimation of prediction mean square error to control splitting. The goal is an algorithm that will share the best features of linear regression and faster recursive partitioning.

Suggested Citation

  • Min-Ching Huang & Wei-Yin Loh, 1992. "Model Fitting With Tree-Structured Regression," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 374-378, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_57
    DOI: 10.1007/978-1-4612-2856-1_57
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