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Compare similarity/distance metrics: Euclidean, cosine, Manhattan, Jaccard, Mahalanobis.

Pick the wrong distance metric and you quietly break kNN, clustering, and retrieval. The signal is knowing what each metric actually measures and matching it to the data: magnitude vs direction, sets, correlated features.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Pick the wrong distance metric and you quietly break kNN, clustering, and retrieval. The signal is knowing what each metric actually measures and matching it to the data: magnitude vs direction, sets, correlated features.

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