Splitting on categorical predictors in random forests
One reason for the widespread success of random forests (RFs) is their ability to analyze most datasets without preprocessing. For example, in contrast to many other statistical methods and machine learning approaches, no recoding such as dummy coding is required to handle ordinal and nominal predic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2019-02-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/6339.pdf |