论文标题
在基于树的模型中概括为特征选择的增益惩罚
Generalizing Gain Penalization for Feature Selection in Tree-based Models
论文作者
论文摘要
我们通过基于树的模型中的增益来开发一种新的特征选择方法。首先，我们表明以前的方法没有执行足够的正则化，并且经常表现出优化的样本外部性能，尤其是在存在相关特征时。取而代之的是，我们开发了一种新的收益惩罚思想，该想法为基于树的模型展示了一般的本地全球正则化。新方法可以在选择特定特定的重要性权重时更灵活。我们在模拟和真实数据上验证了我们的方法，并实现了流行的R软件包Ranger的扩展。
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.
