In algorithms relying on distance metrics, features with larger numerical ranges will disproportionately influence the total distance. The feature with the larger scale will dominate the distance metric, regardless of its actual predictive importance, because its coordinate differences are numerically much larger.
Conditions: Applies to distance-based machine learning algorithms like KNN or K-means.; Features are not scaled or normalized.; Distance is computed using a standard metric like Euclidean distance.
In algorithms relying on distance metrics, features with larger numerical ranges will disproportionately influence the total distance. The feature with the larger scale will dominate the distance metric, regardless of its actual predictive importance, because its coordinate differences are numerically much larger.
Conditions: Applies to distance-based machine learning algorithms like KNN or K-means.; Features are not scaled or normalized.; Distance is computed using a standard metric like Euclidean distance.