Normalize numerical features with standard, min-max, robust, and max-abs scalers to bring columns to a common range before model training.
The Scaling component brings all selected numerical features onto the same scale, which improves the performance of distance-based and gradient-based models.
Tree-based models (Random Forest, Decision Tree, Gradient Boosting) do not require scaling. Apply scaling before SVM, KNN, Logistic Regression, and Linear Regression.
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