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Learn AI with Python · Lesson

SVMs for Regression (SVR)

SVR epsilon tube, nu-SVR, comparing SVR to linear regression on non-linear data.

SVMs Can Do Regression Too

The margin idea extends to regression. Support Vector Regression (SVR) fits a function while keeping predictions within a tolerance band, ignoring small errors.

The Epsilon-Insensitive Tube

SVR draws an epsilon tube around the predicted function. Points inside the tube contribute zero loss; only points outside are penalized. This focuses the model on meaningful errors.

All lessons in this course

  1. SVM Theory: Margins and Support Vectors
  2. Kernel Trick: RBF, Polynomial, and Sigmoid
  3. SVMs for Classification with sklearn
  4. SVMs for Regression (SVR)
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