How to tune the hyperparameter of svm?

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Accepted Answer

Hyperparameters of SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.

Grid search involves defining a grid of hyperparameter values and evaluating the model performance for each combination.
Random search randomly selects hyperparameter values from a defined range and evaluates the model performance.
Bayesian optimization uses a probabilistic model to select the next set of hyperparameters based on previous evaluations.
Cross-validation is commonly used to estimate the performance of different hyperparameter settings.
Examples: tuning C (regularization parameter) and gamma (kernel coefficient) in SVM.