> ## 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 Classifier component

> Train a random forest ensemble that aggregates many decision trees through bagging to deliver robust, accurate classification on tabular data.

**Random Forest** trains many decision trees on random subsets of your data and combines their votes into a final prediction. It handles mixed feature types well, is resistant to overfitting, and requires minimal tuning.

## Configuration

| Parameter             | Description                                                         | Default |
| --------------------- | ------------------------------------------------------------------- | ------- |
| **N Estimators**      | Number of trees in the forest. More trees = more stable but slower. | `100`   |
| **Max Depth**         | Maximum depth of each tree. Leave empty for unlimited.              | None    |
| **Min Samples Split** | Minimum samples required to split a node.                           | `2`     |
| **Min Samples Leaf**  | Minimum samples required in a leaf node.                            | `1`     |
| **Random State**      | Seed for reproducibility.                                           | `42`    |

## Input / Output

|        | Type                                          |
| ------ | --------------------------------------------- |
| Input  | `X Train` + `Y Train` (from Train-Test Split) |
| Output | Trained Model                                 |

## When to use

A good default choice for most classification problems. Works well on tabular data without feature scaling.