> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Decision Tree Regressor component

> Train an interpretable decision tree regressor that predicts continuous targets by partitioning feature space, with tunable depth and split criteria.

**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.

## 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         |

## When to use

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