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.