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• W3034921807 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." @default.
• W3034921807 created "2020-06-19" @default.
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• W3034921807 date "2020-06-12" @default.
• W3034921807 modified "2023-09-26" @default.
• W3034921807 title "Generalizing Gain Penalization for Feature Selection in Tree-based Models." @default.
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• W3034921807 hasPublicationYear "2020" @default.
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