Linear Support Vector Machine Regressor

Linear Support Vector Machine Regressor is similar to Support Vector Machine Regressor with the linear kernel but has more flexibility in the choice of penalties and loss functions.

Advantages:

• It performs well in higher dimension spaces
• It is robust to outliers

Disadvantages:

• It is computationally expensive
• It is tricky in selecting the appropriate kernel function