Release 12 (from main)

models/linear_regression/README.md

@@ -11,8 +11,8 @@ These parameters are defined in the `model.yaml` under `model.input_map` and are
 | `degree`           | `2`     | Degree of the polynomial used to create polynomial features.               |
 | `interaction_only` | `true`  | Whether to include only interaction features in polynomial features.       |
 | `verbose`          | `true`  | Enable verbose output during model execution.                              |
-| `clipping_max`     | `100`   | Maximum value for clipping prediction output.                              |
-| `clipping_min`     | `0`     | Minimum value for clipping prediction output.                              |
+| `clipping_max`     | `100`   | Optional maximum for prediction output (number, decimals allowed).         |
+| `clipping_min`     | `0`     | Optional minimum for prediction output (number, decimals allowed).         |
 
 ## Data Preprocessor (`DataPreprocessor`)
 
@@ -59,7 +59,7 @@ Neither method mutates the passed DataFrames.
 - If `variable_columns` is not set, it is inferred during `fit` as all columns except the target.
 - Polynomial features are created automatically when `degree > 1`.
 - Rows with NaN in any model input get a NaN prediction (the output always has one value per input row); rows with NaN features or NaN target are left out of the fit.
-- `clipping` uses Q1/Q3 of the training target when replacing out-of-bounds predictions.
+- `clipping` uses Q1/Q3 of the training target when replacing out-of-bounds predictions: a prediction above `clipping_max` becomes Q3, one below `clipping_min` becomes Q1 (a prediction equal to the limit is kept). Each limit is optional and independent — set only one and only that side is checked.
 - The wrapper enforces that `wrapper.target` is set before training or retraining; otherwise, a `ValueError` is raised to fail fast when the configuration is incomplete.
 
 All parameters documented above **must stay in sync** between: