Classification, Support Vector Machine, Roc Curve, Rbf Kernels.
GridSearchCV(estimator=SVC(), param_grid=grid, cv= 5) svm_cv_r.fit(X_tr_scaled, y_train) svm_cv_r.best_params_ {'C': 1.0, 'gamma': 10, 'kernel': 'rbf', 'random_state': 21} The best parameter values are obtained when gamma of the RBF kernel is 10.0 and the value of the penalty C=1.0. We now fit the