coef_ : array, shape = [n_class * (n_class-1) / 2, n_features] Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is a readonly property derived from dual_coef_ and support_vectors_.
I don't understand this. If I use PolynomialFeatures on X, I can use a linear kernel and read the resulting coefficients/weights. Is the polynomial kernel the same thing as using PolynomialFeatures? How do you read the coef_ values for the fit() on the training data?
How is gamma related to this? Isn't gamma:
Gamma = 1/2*sigma^2
How is that related to a polynomial? I'ts not only an rbf thing?
