Effect Of Feature Scaling On Gradient Descent

Feature scaling, such as normalization or standardization, ensures that all features are on a similar scale, allowing gradient descent to avoid disproportionately large updates.

When the features have different scales, gradient descent may take longer to converge or even fail to converge at all. Feature scaling, such as normalization or standardization, ensures that all features are on a similar scale, allowing gradient descent to avoid disproportionately large updates based on features with larger values.