sum_employee.py

# Build SVM classifier
clf_svc = make_pipeline(StandardScaler(),
                        SVC(C=0.01,
                            gamma=0.1,
                            kernel="poly",
                            degree=5,
                            coef0=10,
                            probability=True))
clf_svc.fit(X_train, y_train)
svc_cv_scores = cross_val_score(clf_svc,
                                X=X_train,
                                y=y_train,
                                scoring="f1",
                                cv=10,
                                n_jobs=-1)
# Print CV
print(f"\033[1m\033[94mThe 10-folds CV f1-score is: {np.mean(svc_cv_scores) * 100:.2f}%")