> ## 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 Classifier component

> Train a gradient boosting classifier that sequentially fits decision trees to correct prior errors, delivering strong accuracy on tabular data.

**Gradient Boosting** builds trees one at a time, with each tree correcting the mistakes of the previous ensemble. It often achieves higher accuracy than Random Forest but requires more careful tuning.

## Configuration

| Parameter         | Description                                                                                                 | Default |
| ----------------- | ----------------------------------------------------------------------------------------------------------- | ------- |
| **N Estimators**  | Number of boosting stages (trees).                                                                          | `100`   |
| **Learning Rate** | Shrinks the contribution of each tree. Lower = more conservative, needs more trees.                         | `0.1`   |
| **Max Depth**     | Maximum depth of individual trees.                                                                          | `3`     |
| **Subsample**     | Fraction of training samples used per tree. Values below 1.0 add randomness (stochastic gradient boosting). | `1.0`   |
| **Random State**  | Seed for reproducibility.                                                                                   | `42`    |

## Input / Output

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

## When to use

Strong choice when accuracy is the primary goal. Slower to train than Random Forest. Does not require scaling.