Machine Learning

Import libraries from sklearn.model_selection import cross_validate Cross validate scores = cross_validate(rf, train_x, train_y, return_train_score=True, n_jobs=-1) print("cross_validate") print(np.mean(scores['train_score']), np.mean(scores['test_score']))

Import libraries from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_validate Hyperparameter tuning param={ "min_samples_leaf":[1, 2, 3, 4, 5, 6, 7, 8, 9], "max_depth":[2, 3, 4, 5, 6, None], "min_samples_split":[2, 3, 4, 5, 6, 7, 8, 9, 10] } gs = GridSearchCV(DecisionTreeClassifier(), param, n_jobs =-1) gs.fit(X, Y) dt = gs.best_estimator_ print(dt.score(tes..