Feature scaling is needed because raw features often differ in units and magnitudes, making them incomparable. Scaling brings them onto a common scale, which is necessary for many machine learning algorithms to function properly.
Conditions: Dataset contains multiple features with different units or ranges.; The model is sensitive to feature magnitudes (e.g., distance-based or gradient-based).
Feature scaling is needed because raw features often differ in units and magnitudes, making them incomparable. Scaling brings them onto a common scale, which is necessary for many machine learning algorithms to function properly.
Conditions: Dataset contains multiple features with different units or ranges.; The model is sensitive to feature magnitudes (e.g., distance-based or gradient-based).