> ## 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 Classifier component

> Train an interpretable decision tree classifier that splits data by feature thresholds to assign class labels, with tunable depth and split criteria.

**Decision Tree** learns a set of if/then rules from your data. It's the most interpretable model in the toolbox — you can trace exactly why a prediction was made.

## Configuration

| Parameter             | Description                                                                       | Default |
| --------------------- | --------------------------------------------------------------------------------- | ------- |
| **Max Depth**         | Maximum tree depth. Shallower trees generalize better. Leave empty for unlimited. | 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: `gini` or `entropy`                                        | `gini`  |
| **Random State**      | Seed for reproducibility.                                                         | `42`    |

## Input / Output

|        | Type                  |
| ------ | --------------------- |
| Input  | `X Train` + `Y Train` |
| Output | Trained Model         |

## When to use

Good for understanding feature importance and building interpretable models. Does not require scaling. Prone to overfitting — use **Max Depth** to control tree size.