Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
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The paper delivers an optimized Python implementation of Categorical Gini Correlation for computing dependence measures, confidence intervals, and independence tests.
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Comparing Two Categorical Gini Correlations with Applications to Classification Problems
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
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gcor: A Python Implementation of Categorical Gini Correlation and Its Inference
The paper delivers an optimized Python implementation of Categorical Gini Correlation for computing dependence measures, confidence intervals, and independence tests.