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).
Decision-tree models do not require feature scaling because they split data based on feature conditions (thresholds) rather than absolute magnitudes. Monotonic rescaling of feature values does not change the branching structure or the order of splits.
Conditions: Model is a decision tree or an ensemble of trees (random forest, XGBoost, etc.).; Splitting logic is based on comparing feature values to thresholds.
Decision-tree models do not require feature scaling because they split data based on feature conditions (thresholds) rather than absolute magnitudes. Monotonic rescaling of feature values does not change the branching structure or the order of splits.
Conditions: Model is a decision tree or an ensemble of trees (random forest, XGBoost, etc.).; Splitting logic is based on comparing feature values to thresholds.