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).