Hidden Variables unseen by Random Forests

Autor:	Blum, Ricardo, Hiabu, Munir, Mammen, Enno, Meyer, Joseph Theo
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the conventional CART criterion struggles to capture during tree construction. We argue that simple alternative partitioning schemes used in the tree growing procedure can enhance identification of these interactions. In a simulation study we compare these variants to conventional Random Forests and Extremely Randomized trees. Our results validate that the modifications considered enhance the model's fitting ability in scenarios where pure interactions play a crucial role. Comment: arXiv admin note: substantial text overlap with arXiv:2309.01460
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.15500 Zobrazit plný text záznamu View this record from Arxiv