How do scaling transformations affect distance-based algorithms?

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.