I am working on a classification program using SVM RBF kernel. To find the best parameters C and gamma, I used grid search, and got the image below. What confuses me is that when gamma varies from 0.3 to 3, the accuracy changes so rapidly. I wonder what happens in this region.
I think the good models should be found on the diagonal, of a higher C with a lower gamma , or a lower C with a higher gamma.
Could anyone help me to explain the performance variation when gamma is between 0.3 and 3?
I didn't do feature scaling with the first image so the features are not scaled. I guess it could be a reason because when I normalized the data, the image turned out to be like
the yellow part between gamma=1 and 10 is gone and the accuracy seems decreases slightly