人工智能和机器学习之回归算法：随机森林回归的数学基础.docx

X = boston.data

y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf = RandomForestRegressor(n_estimators=100, random_state=42)

rf.fit(X_train, y_train)

y_pred = rf.predict(X_test)

from sklearn.datasets import load_boston

from sklearn.tree import DecisionTreeRegressor

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

regressor = DecisionTreeRegressor(random_state=42)

regressor.fit(X_train, y_train)

y_pred = regressor.predict(X_test)

mse = mean_squared_error(y_test, y_pred)

print(f'Mean Squared Error: {mse}')

regressor = DecisionTreeRegressor(criterion='mse', random_state=42)

regressor = DecisionTreeRegressor(criterion='friedman_mse', random_state=42)