ML11: 决策树DecisionTreeRegressor 自适应增强决策树AdaBoostRegressor 波士顿房价预测

import sklearn.datasets as sd 
import sklearn.utils as su 
import sklearn.tree as st 
import sklearn.ensemble as se 
import sklearn.metrics as sm 
housing = sd.load_boston()
x,y = su.shuffle(housing.data,housing.target,random_state=7)
train_size = int(len(x)*0.8)
train_x,test_x,train_y,test_y = x[:train_size],x[train_size:],y[:train_size],y[train_size:]
model = st.DecisionTreeRegressor(max_depth = 4)
model.fit(train_x,train_y)
pre_test_y = model.predict(test_x)
print(sm.r2_score(test_y,pre_test_y))
model = se.AdaBoostRegressor(max_depth=4,n_estimators = 400,random_state=7)
model.fit(train_x,train_y)
pre_test_y = model.predict(train_x)
pint(sm.r2_score(train_y,pre_test_y))