> ## 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.

# Random Forest Regressor component

> Train a random forest ensemble that aggregates many decision trees through bagging to predict continuous values with strong accuracy and stability.

**Random Forest Regressor** averages the predictions of many decision trees trained on random data subsets. It handles non-linear relationships well and is robust to noisy data.

## Configuration

| Parameter             | Description                                            | Default |
| --------------------- | ------------------------------------------------------ | ------- |
| **N Estimators**      | Number of trees in the forest.                         | `100`   |
| **Max Depth**         | Maximum depth of each tree. 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`     |
| **Random State**      | Seed for reproducibility.                              | `42`    |

## Input / Output

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

## When to use

A strong default for regression on tabular data. Does not require feature scaling. Works well out of the box without extensive tuning.