Split Criteria is a decision rule in the context of decision tree algorithms that determines the optimal feature and threshold for partitioning data at each internal node, distinguishing it from other tree construction mechanisms by its role in maximizing information gain or minimizing impurity to guide the recursive segmentation of the dataset.
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The effect of splitting on random forests
Hemant Ishwaran · Machine Learning · 2014 · 231 citations · Full text
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University of California, Berkeley
Berkeley, United States
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Częstochowa University of Technology
Częstochowa, Poland
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Yale University
New Haven, United States
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192 citations
Indian Institute of Technology Delhi
New Delhi, India
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Beijing, China
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Guru Jambheshwar University of Science and Technology
Hisar, India
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Duke University
Durham, United States
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Minxiong, Taiwan
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2 publications · 213 citations
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IJIDS · 2 publications · 267 citations
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