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.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.
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 |