kin_employee.py

# Build KNN classifier
pip_knn = make_pipeline(StandardScaler(), KNeighborsClassifier())
hyperparam_range = range(1, 20)
gs_knn = GridSearchCV(pip_knn,
                      param_grid={"kneighborsclassifier__n_neighbors": hyperparam_range,
                                  "kneighborsclassifier__weights": ["uniform", "distance"]},
                      scoring="f1",
                      cv=10,
                      n_jobs=-1)
gs_knn.fit(X_train, y_train)
print(f"\033[1m\033[0mThe best hyperparameters:\n{'-' * 25}")
for hyperparam in gs_knn.best_params_.keys():
    print(hyperparam[hyperparam.find("__") + 2:], ": ", gs_knn.best_params_[hyperparam])
print("\033[1m" + "\033[94m" + "Best 10-folds CV f1-score: {:.2f}%.".format((gs_knn.best_score_) * 100))