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.
Gradient descent oscillates when features are not scaled because features with vastly different ranges cause the gradients with respect to their corresponding parameters to differ greatly. This leads to small step sizes for parameters of small-scale features and large step sizes for parameters of large-scale features, resulting in an inefficient, zigzagging path toward the minimum.
Conditions: Used in linear regression optimization with gradient descent.; Features have significantly different scales (e.g., one ranges from 0 to 1, another from 100 to 1000).
Gradient descent oscillates when features are not scaled because features with vastly different ranges cause the gradients with respect to their corresponding parameters to differ greatly. This leads to small step sizes for parameters of small-scale features and large step sizes for parameters of large-scale features, resulting in an inefficient, zigzagging path toward the minimum.
Conditions: Used in linear regression optimization with gradient descent.; Features have significantly different scales (e.g., one ranges from 0 to 1, another from 100 to 1000).
Feature scaling is important primarily to achieve faster convergence in optimization algorithms like gradient descent, and to ensure that distance-based algorithms compute distances appropriately without larger-scale features dominating.
Conditions: Applies to iterative optimization algorithms like gradient descent.; Applies to distance-based machine learning algorithms like KNN or K-means.
Feature scaling is important primarily to achieve faster convergence in optimization algorithms like gradient descent, and to ensure that distance-based algorithms compute distances appropriately without larger-scale features dominating.
Conditions: Applies to iterative optimization algorithms like gradient descent.; Applies to distance-based machine learning algorithms like KNN or K-means.