聚类特征重要性(演示幻灯片)

Computation Theory eJournal Pub Date : 2020-01-29 DOI:10.2139/ssrn.3517595

Marcos M. López de Prado

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引用次数: 1

摘要

当两个或多个解释变量共享大量信息(预测能力)时，就会发生替代效应。在存在替代效应的情况下，特征重要性方法可能无法稳健地确定哪些变量是显著的。本文讨论了聚类特征重要性(CFI)方法，该方法对线性和非线性替代效应都具有鲁棒性。

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Clustered Feature Importance (Presentation Slides)

A substitution effect takes place when two or more explanatory variables share a substantial amount of information (predictive power). Under the presence of substitution effects, feature importance methods may not be able to determine robustly which variables are significant. This presentation discusses the Clustered Feature Importance (CFI) method, which is robust to linear as well as non-linear substitution effects.

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Computation Theory eJournal

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