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.
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).
No, standardization does not change the general shape of its distribution; it only shifts the mean to zero and scales the standard deviation to one. It is a linear transformation, so a highly skewed distribution remains skewed after standardization.
Conditions: The dataset has a well-defined mean and standard deviation.; Standardization is applied as a linear transformation.
No, standardization does not change the general shape of its distribution; it only shifts the mean to zero and scales the standard deviation to one. It is a linear transformation, so a highly skewed distribution remains skewed after standardization.
Conditions: The dataset has a well-defined mean and standard deviation.; Standardization is applied as a linear transformation.