AI RESEARCH
Conditional Local Importance by Quantile Expectations
arXiv CS.LG
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ArXi:2411.08821v3 Announce Type: replace-cross Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to individual observations. Current, popular methods, including LIME and SHAP, typically fail to accurately reflect locally dependent relationships between variables and instead focus on marginal importance values. Additionally, they are not natively adapted for multi-class classification problems.