svm-hyperparameter-selection-grid-search-cv

IN premise — entries/2026/06/21/wiki-Support_vector_machine-chunk-4.md

Created 2026-06-21T09:55:54+00:00

SVM hyperparameters (kernel choice, gamma, lambda/C) are typically selected via grid search with exponentially growing sequences (e.g., 2^-5 to 2^15) evaluated by cross-validation, or alternatively by Bayesian optimization.

Dependents

These beliefs depend on this one:

• IN svm-codified-practical-methodology — SVMs have an unusually prescriptive practical methodology for ML: standardize features first, default to RBF kernel, then grid-search C and gamma with cross-validation.