> ## 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.

# Support Vector Regressor (SVR) component

> Train a support vector regressor that fits an epsilon-insensitive tube to predict continuous values, with linear, polynomial, and RBF kernels.

**SVR** finds a function that fits as many data points as possible within an epsilon-wide tube around the prediction. Points outside the tube are penalized.

## Configuration

| Parameter   | Description                                                                             | Default |
| ----------- | --------------------------------------------------------------------------------------- | ------- |
| **C**       | Regularization parameter. Higher = less regularization, tighter fit.                    | `1.0`   |
| **Kernel**  | Kernel function: `rbf`, `linear`, `poly`, `sigmoid`                                     | `rbf`   |
| **Gamma**   | Kernel coefficient: `scale`, `auto`, or a float.                                        | `scale` |
| **Epsilon** | Width of the insensitive tube. Predictions within ε of the true value incur no penalty. | `0.1`   |

## Input / Output

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

## When to use

Effective on small-to-medium datasets with non-linear relationships. **Always scale features** before SVR.