While running SVC(), how we can hyperparameter tune C vs gamma combination?
I could see changes in C and gamma are impacting the accuracy differently. Also, what I understand about C and gamma are:
C is the cost of mis-classification which means a large C gives you a low bias and high variance, low bias because you penalize the cost of mis-classification a lot. And a small C gives you a higher bias and lower variance.
Gamma controls the shape of the "peaks" where you raise the points. A small gamma gives you a pointed bump in the higher dimensions, while a large gamma gives you a softer, broader bump. So a small gamma will give you a low bias and high variance, while a large gamma will give you a higher bias and low variance.