# How to interpret a SVM plot

Hi, so i'm using support vector machine for some statistical project and this is a plot of from a using a sigmoid kernel. There are 9 variables and 300 data points. The way i interpret this plot is that the value of eoef0 and gamma will be taken from the darkest region. What does the color of the region signify? The error rate?

The second plot is that of using the radial basis function. Was wondering where are the plot different given that they are still svm methods but just utilizing different kernel.

Sigmoid kernel has two parameters, $\gamma$ and coef0 $K(X, Y) = \tanh(γ\cdot X^TY + coef0)$
Gaussian (radial basis kernel) has only one parameter, $\gamma$
$K(X, Y) = \exp(\gamma\|X-Y\|^2)$