论文标题
剩余外部的选择性级联
Selective Cascade of Residual ExtraTrees
论文作者
论文摘要
我们提出了一种新型基于树的集合方法，名为“剩余余额”的选择性级联（得分）。得分从表示学习中汲取灵感，将正则回归与可变选择功能结合在一起，并利用增强来改善预测和减少概括错误。我们还开发了一个可变的重要性度量，以提高分数的解释性。我们的计算机实验表明，得分在针对外界，随机森林，梯度增强机和神经网络的预测方面提供了可比或卓越的性能。所提出的分数的可变重要性度量与研究的基准方法相当。最后，分数的预测性能在超参数值之间保持稳定，这表明对超参数规范的潜在鲁棒性。
We propose a novel tree-based ensemble method named Selective Cascade of Residual ExtraTrees (SCORE). SCORE draws inspiration from representation learning, incorporates regularized regression with variable selection features, and utilizes boosting to improve prediction and reduce generalization errors. We also develop a variable importance measure to increase the explainability of SCORE. Our computer experiments show that SCORE provides comparable or superior performance in prediction against ExtraTrees, random forest, gradient boosting machine, and neural networks; and the proposed variable importance measure for SCORE is comparable to studied benchmark methods. Finally, the predictive performance of SCORE remains stable across hyper-parameter values, suggesting potential robustness to hyperparameter specification.
