grid-search with accuracy best parameters{'gamma' best cross-validation score (accuracy)) test set auc test set accuracy in[ ]using auc scoring insteadgrid gridsearchcv(svc()param_grid=param_gridscoring="roc_auc"grid fit(x_trainy_trainprint("\ngrid-search with auc"print("best parameters:"grid best_params_print("best cross-validation score (auc){ }format(grid best_score_)print("test set auc{ }formatroc_auc_score(y_testgrid decision_function(x_test)))print("test set accuracy{ }format(grid score(x_testy_test))out[ ]grid-search with auc best parameters{'gamma' best cross-validation score (auc) test set auc test set accuracy when using accuracythe parameter gamma= is selectedwhile gamma= is selected when using auc the cross-validation accuracy is consistent with the test set accuracy in both cases howeverusing auc found better parameter setting in terms of auc and even in terms of accuracy the most important values for the scoring parameter for classification are accuracy (the default)roc_auc for the area under the roc curveaverage_precision for the area under the precision-recall curvef _macrof _microand _weighted for the binary -score and the different weighted variants for regressionthe most commonly used values are for the scoremean_squared_error for mean squared errorand mean_absolute_error for mean absolute error you can find full list of supported arguments in the documentation or by looking at the scorer dictionary defined in the metrics scorer module finding higher-accuracy solution using auc is likely consequence of accuracy being bad measure of model performance on imbalanced data evaluation metrics and scoring