Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data

In three linesComparative study of local explainability techniques (LIME, SHAP, Feature Ablation) reliability across 32 tabular datasets. Results show explanation quality does not systematically correlate with model predictive performance, but depends instead on dataset complexity and feature distributions.

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