树模型中特征选择的广义增益惩罚
Generalizing Gain Penalization for Feature Selection in Tree-based Models
摘要：
我们开发了一种新的方法，通过在基于树的模型中的增益惩罚的特征选择。首先，我们表明，以前的方法没有执行足够的正则化，往往表现出次优的样本外的性能，特别是当相关的功能存在。相反，我们开发了一个新的增益惩罚的想法，表现出一般的局部-全局正则化基于树的模型。新方法允许在选择特定特征的重要性权重时具有更大的灵活性。我们验证了我们的方法在模拟和真实的数据，并实现作为一个流行的R包测距仪的扩展。
英文摘要：
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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