How does SVM adapt to regression (SVR)?
mediumAnswer
- SVR uses an epsilon-insensitive loss: residuals | ≤ epsilon are free (zero penalty), residuals outside are penalized linearly (like L1).
- Combined with the kernel trick, it fits a flexible non-linear function using only a subset of training points as support vectors.
- Tune C (violation penalty), epsilon (tube width), and gamma (for RBF).
- Works well on small/medium tabular problems where linear methods underfit and boosted trees are overkill.
Check yourself — multiple choice
- SVR uses squared error loss
- SVR uses an epsilon-insensitive loss and support vectors just like classification SVMs
- SVR only works with linear kernels
- SVR cannot handle nonlinearities
SVR = ε-insensitive tube + kernel + SVs. Nonlinear regression via kernels.
#svm#regression
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