What is the effect of gamma vs C interaction in SVM?

Gamma controls locality of influence while C controls penalty for misclassification, and together they define model complexity.

High gamma creates highly localized decision regions, while high C forces strict classification of training data. Together, high gamma + high C often leads to overfitting, while low gamma + low C leads to underfitting. Proper tuning balances bias-variance tradeoff.