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