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.
Feature scaling should be used for algorithms that rely on Euclidean distance or gradient-based optimization, such as KNN, K-means, linear regression, logistic regression, and neural networks. It is generally not needed for tree-based models like decision trees, random forests, and XGBoost.
Conditions: Model uses distance metrics or iterative optimization sensitive to feature scale.; Model is not tree-based or an ensemble of trees.
Feature scaling should be used for algorithms that rely on Euclidean distance or gradient-based optimization, such as KNN, K-means, linear regression, logistic regression, and neural networks. It is generally not needed for tree-based models like decision trees, random forests, and XGBoost.
Conditions: Model uses distance metrics or iterative optimization sensitive to feature scale.; Model is not tree-based or an ensemble of trees.