This table provides detailed statistical distance metrics between baseline and target distributions for each feature.

Feature Type KL Divergence JS Distance Wasserstein Distance Hellinger Distance Shift Severity
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Distance Metrics Interpretation

Metric Range Minor Shift Moderate Shift Significant Shift Description
KL Divergence [0, ∞) 0 - 0.5 0.5 - 2.0 > 2.0 Measures information gain when updating from baseline to target distribution
JS Distance [0, 1] 0 - 0.2 0.2 - 0.4 > 0.4 Symmetric measure of similarity between distributions
Wasserstein [0, ∞) 0 - 0.1 0.1 - 0.3 > 0.3 Earth mover's distance between distributions
Hellinger [0, 1] 0 - 0.2 0.2 - 0.5 > 0.5 Probabilistic measure of distribution similarity
⚠️ Different distance metrics may be more appropriate for different feature types. For numerical features, Wasserstein distance is often more interpretable. For categorical features, KL divergence or JS distance are typically used.