> ## 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 Classifier (SVC) component

> Train a support vector classifier that finds the maximum-margin hyperplane separating classes, with linear, polynomial, and RBF kernels.

**SVC** finds the decision boundary that maximizes the margin between classes. With non-linear kernels it can model complex class boundaries.

## Configuration

| Parameter  | Description                                                                                                  | Default |
| ---------- | ------------------------------------------------------------------------------------------------------------ | ------- |
| **C**      | Regularization parameter. Higher values = less regularization, fits training data more tightly.              | `1.0`   |
| **Kernel** | Kernel function: `rbf`, `linear`, `poly`, `sigmoid`                                                          | `rbf`   |
| **Gamma**  | Kernel coefficient: `scale`, `auto`, or a float. Controls the influence radius of a single training example. | `scale` |

## Input / Output

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

## When to use

Works well on small-to-medium datasets with clear class boundaries. **Always scale features** before SVC — it is very sensitive to feature magnitudes.