The Scaling component brings all selected numerical features onto the same scale, which improves the performance of distance-based and gradient-based models.Documentation Index
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Configuration
| Option | Description |
|---|---|
| Method | Scaling algorithm to apply (see table below). |
| Features | Numerical columns to scale. |
Methods
| Method | How it works | Best for |
|---|---|---|
| Standard Scaling | Subtracts mean, divides by std dev. Output has mean=0, std=1. | Most models — SVM, Logistic Regression, KNN |
| MinMax Scaling | Rescales values to the [0, 1] range. | Neural networks, algorithms sensitive to magnitude |
| Robust Scaling | Uses median and IQR instead of mean and std dev. | Data with significant outliers |
Input / Output
| Type | |
|---|---|
| Input | DataFrame |
| Output | DataFrame |
Tree-based models (Random Forest, Decision Tree, Gradient Boosting) do not require scaling. Apply scaling before SVM, KNN, Logistic Regression, and Linear Regression.