How do scaling transformations affect distance-based algorithms?

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How do scaling transformations affect distance-based algorithms?

Asked on Nov 09, 2025

Answer

Scaling transformations are crucial in distance-based algorithms, such as k-nearest neighbors (KNN) and clustering methods like k-means, because these algorithms rely on distance calculations. If features are on different scales, those with larger ranges can disproportionately influence the results. Scaling ensures that each feature contributes equally to the distance computation.

Example Concept: Scaling transformations, such as standardization (z-score normalization) or min-max scaling, adjust the feature values to a common scale without distorting differences in the ranges of values. In k-means clustering, for instance, scaling helps ensure that the algorithm's distance calculations accurately reflect the true differences between data points, leading to more meaningful clusters. Similarly, in KNN, scaling prevents features with larger ranges from dominating the distance metric, thus improving the algorithm's performance and accuracy.

Additional Comment:

Standardization transforms data to have a mean of 0 and a standard deviation of 1.
Min-max scaling rescales data to a fixed range, typically [0, 1].
Scaling is particularly important when features have different units (e.g., height in cm and weight in kg).
Always apply the same scaling transformation to both training and test datasets.

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