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.

中文翻译：

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剩余额外树的选择性级联

我们提出了一种新的基于树的集成方法，名为 Selective Cascade of Residual ExtraTrees (SCORE)。SCORE 从表征学习中汲取灵感，将正则化回归与变量选择特征相结合，并利用 boosting 来改进预测并减少泛化错误。我们还开发了一个可变重要性度量来提高 SCORE 的可解释性。我们的计算机实验表明，SCORE 在针对 ExtraTrees、随机森林、梯度提升机和神经网络的预测方面提供了可比或卓越的性能；并且建议的 SCORE 变量重要性度量与研究的基准方法相当。最后，SCORE 的预测性能在超参数值之间保持稳定，表明对超参数规范的潜在鲁棒性。