Support Vector Classifier (SVC) component - ClickML Studio

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.