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Decision Trees

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Abstract

In this chapter, we construct decision trees by estimating the relationship between the covariates and the response from observed data. Starting from the root, each vertex traces to either the left or right at each branch, depending on whether a condition w.r.t. the covariates is met, and finally reaches a terminal node to obtain the response. Compared with the methods we have considered thus far, since it is expressed as a simple structure, the estimation accuracy of a decision tree is poor, but since it is expressed visually, it is easy to understand the relationship between the covariates and the response. Decision trees are often used to understand relationships rather than to predict the future, and decision trees can be used for regression and classification. The decision tree has the problem that the estimated tree shapes differ greatly even if observation data that follow the same distribution are used. Therefore, similar to the bootstrap discussed in Chap. 4, by sampling data of the same size from the original data multiple times, we reduce the variation in the obtained decision tree and this improvement can be considered. Finally, we introduce a method (boosting) that produces many small decision trees in the same way as the backfitting method learned in Chap. 7 to make highly accurate predictions.

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  • DOI: 10.1007/978-981-15-7877-9_8
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Suzuki, J. (2021). Decision Trees. In: Statistical Learning with Math and Python. Springer, Singapore. https://doi.org/10.1007/978-981-15-7877-9_8

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  • DOI: https://doi.org/10.1007/978-981-15-7877-9_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7876-2

  • Online ISBN: 978-981-15-7877-9

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