x, y -> svm -> label
z= x^2 + y^2
Kernel Trick
x, y -> x1, x2, x3, x4, x5
SVM γ(gamma) parameter
γ- define how far the influence of single training example reaches
low values – far
high values – close
Overfitting: stop overfitting
features_train = features_train[:len(features_train)/100]
labels_train = labels_train[:len(labels_train)/100]
