Support Vector Regressor (SVR) component - ClickML Studio

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.