Decision Tree Regressor splits the training data into regions and predicts the mean target value within each region. It’s the most interpretable regression model.Documentation Index
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Configuration
| Parameter | Description | Default |
|---|---|---|
| Max Depth | Maximum tree depth. Shallower = less overfitting. | None |
| Min Samples Split | Minimum samples to split a node. | 2 |
| Min Samples Leaf | Minimum samples in a leaf node. | 1 |
| Criterion | Split quality measure: squared_error, friedman_mse, absolute_error, poisson | squared_error |
| Random State | Seed for reproducibility. | 42 |
Input / Output
| Type | |
|---|---|
| Input | X Train + Y Train |
| Output | Trained Model |