> ## 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.

# Gradient Boosting Regressor component

> Train a gradient boosting regressor that sequentially fits decision trees to minimize prediction error, achieving strong accuracy on tabular targets.

**Gradient Boosting Regressor** builds trees iteratively, where each tree corrects the residual errors of the current ensemble. It typically achieves the best accuracy among regression models in the toolbox.

## Configuration

| Parameter         | Description                                                              | Default |
| ----------------- | ------------------------------------------------------------------------ | ------- |
| **N Estimators**  | Number of boosting stages.                                               | `100`   |
| **Learning Rate** | Contribution of each tree. Lower = more conservative, more trees needed. | `0.1`   |
| **Max Depth**     | Maximum depth of individual trees.                                       | `3`     |
| **Subsample**     | Fraction of samples used per tree. Below 1.0 adds stochasticity.         | `1.0`   |
| **Random State**  | Seed for reproducibility.                                                | `42`    |

## Input / Output

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

## When to use

Best when accuracy is the priority and training time is acceptable. Does not require feature scaling.