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Chebyshev distance

As explained in Outlier Detection in Python

Chebyshev distance is the maximum absolute difference between corresponding coordinates of two points.

Chebyshev distance measures the largest absolute difference between corresponding coordinates of two numeric records. It treats two records as similar only when every feature is similar, and as dissimilar when any feature differs substantially. section 9.7.1

As the limiting case of Minkowski distance with an infinite exponent, it emphasizes the largest feature difference more strongly than Euclidean or Manhattan distance. section 9.7.1

Three-feature row comparison

For rows differing by 0.4, 0.2, and 0.5 across three features, the Chebyshev distance is 0.5. The smaller differences do not affect the result. section 9.7.1

How Outlier Detection in Python covers it

How deep it goes
Defines the metric, derives its limiting relationship to Minkowski distance, and works through a three-feature calculation.
Examples
Uses two rows with three numeric features and differences of 0.4, 0.2, and 0.5.
What it assumes
Assumes numeric features and treats normalization as important when comparing feature differences.
What you can do after
Apply it to compare normalized numeric records or support distance-based outlier detection.
How it connects
Connects Minkowski distance with Manhattan distance, Euclidean distance, Canberra distance, and outlier detection.
Cover of Outlier Detection in Python
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