Classification, Support Vector Machine, Roc Curve, Rbf Kernels.
1000.0], 'random_state' : [21] } svm_cv = GridSearchCV(estimator=SVC(kernel='poly'), param_grid=grid, cv= 5) svm_cv.fit(X_tr_scaled, y_train) svm_cv.best_params_ {'C': 1.0, 'degree': 3, 'kernel': 'poly', 'random_state': 21} The best parameter values are obtained when degree of the polynomial kernel