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arXiv Machine Learning By Robert Milletich, Justin Downes, Steve Goley, Newel Hirst

Conditional Inference Trees and Forests for Feature Selection

Read the original on arXiv Machine Learning →

arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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