Generalizing Gain Penalization for Feature Selection in Tree-based Models

Bruna Wundervald; Andrew Parnell; Katarina Domijan

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arXiv · 2006.07515

Generalizing Gain Penalization for Feature Selection in Tree-based Models

Bruna Wundervald·Andrew Parnell·Katarina Domijan

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Abstract

We develop a new approach for feature selection via gain penalization in tree-based models. First, we show that previous methods do not perform sufficient regularization and often exhibit sub-optimal out-of-sample performance, especially when correlated features are present. Instead, we develop a new gain penalization idea that exhibits a general local-global regularization for tree-based models. The new method allows for more flexibility in the choice of feature-specific importance weights. We validate our method on both simulated and real data and implement itas an extension of the popular R package ranger.

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Bruna Wundervald, Andrew Parnell, Katarina Domijan. 2020-06-12. Generalizing Gain Penalization for Feature Selection in Tree-based Models. https://arxiv.org/abs/2006.07515

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