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- W2914669448 abstract "This chapter focuses on model representation and algorithm modifications that are necessary for the regression task. Regression tree models are hierarchical structures consisting of nodes, branches, and leaves that represent the decomposition of the domain into a number of regions in which target function values can be trivially approximated with sufficient accuracy. The reduced error pruning criterion for regression trees is based on the same idea as its decision tree prototype, which is in fact the simplest possible computational interpretation of the very goal of pruning – to improve the generalization capability of the regression tree. Regression tree models can be created using weighted training instances. Regression trees represent piecewise-constant regression models that decompose the domain into a number of regions and assign a fixed target function value to each region." @default.
- W2914669448 created "2019-02-21" @default.
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- W2914669448 date "2015-01-20" @default.
- W2914669448 modified "2023-09-26" @default.
- W2914669448 title "Regression trees" @default.
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- W2914669448 doi "https://doi.org/10.1002/9781118950951.ch9" @default.
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