The intuitive explanation for the gamma parameter of the RBF kernel in SVMs is the following:
Intuitively, the
gammaparameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. Thegammaparameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors.
https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html#rbf-svm-parameters
In sklearn.svm.SVC the default value of the parameter gamma is 'scale', i.e. gamma = 1 / (n_features * X.var()). What is the explanation for this default choice of gamma and why does it work so well (at least for my dataset, I couldn't beat this value with extensive grid-search for gamma)?
