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What is the intuition behind the Cost and Gamma parameters in SVM?
I was wondering if you could help me understand the intuition behind the Cost and Gamma parameters in SVM?
Of course! The Cost parameter determines the trade-off between achieving a low training error and a low testing error. A small Cost value will result in a wider margin and potentially less overfitting, but more misclassifications in the training set. A large Cost value will result in a narrower margin, potentially more overfitting, but fewer misclassifications in the training set. Gamma, on the other hand, controls the shape of the decision boundary. A small Gamma value will result in a more linear decision boundary, while a large Gamma value will result in a more complex, non-linear decision boundary.