In general I prefer doing:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) clf = RandomForestClassifier(n_estimators = 100, max_depth= None) *clf_f = clf.fit(X_train, y_train)* predicted_labels = clf_f.predict( X_test) score = clf.score(X_test, y_test) score1 = metrics.accuracy_score( y_test, predicted_labels) rather than: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) clf0=RandomForestClassifier(n_estimators=100, max_depth= None) *clf0.fit(X_train, y_train)* y_pred =clf0.predict(X_test) score= metrics.accuracy_score(y_test, y_pred) Are the two codes really equivalent? -- Regards, Joseph Pareti - Artificial Intelligence consultant Joseph Pareti's AI Consulting Services https://www.joepareti54-ai.com/ cell +49 1520 1600 209 cell +39 339 797 0644 -- https://mail.python.org/mailman/listinfo/python-list
