from sklearn.datasets import load_boston
boston = load_boston()#加載boston數據集
import numpy as np
x = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)#25%數據用於測試，75%數據用於訓練
from sklearn.preprocessing import StandardScaler
ss_x = StandardScaler()
ss_y = StandardScaler()
x_train = ss_x.fit_transform(x_train)
x_test = ss_x.transform(x_test)
y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
y_test = ss_y.transform(y_test.reshape(-1, 1))
from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor()
dtr.fit(x_train, y_train.ravel())
dtr_y_predict = dtr.predict(x_test)
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
print('The R-squared value of DecisionTreeRegressor is', dtr.score(x_test, y_test))
print('The mean_squared_error of DecisionTreeRegressor is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dtr_y_predict)))
print('The mean_absolute_error of DecisionTreeRegressor is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dtr_y_predict)))
運行結果如下：
he R-squared value of DecisionTreeRegressor is 0.719653568857
The mean_squared_error of DecisionTreeRegressor is 21.7384251969
The mean_absolute_error of DecisionTreeRegressor is 2.97165354331