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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.

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Configuration

ParameterDescriptionDefault
Max DepthMaximum tree depth. Shallower = less overfitting.None
Min Samples SplitMinimum samples to split a node.2
Min Samples LeafMinimum samples in a leaf node.1
CriterionSplit quality measure: squared_error, friedman_mse, absolute_error, poissonsquared_error
Random StateSeed for reproducibility.42

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Input / Output

Type
InputX Train + Y Train
OutputTrained Model

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When to use

Useful for understanding how predictions are made. Control Max Depth to prevent overfitting. Does not require feature scaling.