"""
1、加载和切分数据
"""
#加载数据
from sklearn.datasets import load_boston
##切分数据
from sklearn.model_selection import train_test_split
X,y = load_boston(return_X_y = True)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
"""
2、加载模型与训练_--KNN 算法
"""
from sklearn.neighbors import KNeighborsRegressor
knn = KNeighborsRegressor()
#训练模型
knn.fit(X=X_train,y= y_train)
"""
3、预测模型
"""
y_pred = knn.predict(X=X_test)
"""
4、评估模型
"""
mse = ((y_pred - y_test)**2).mean() #均方误差，分类模型常用的评估指标
mse
# 上面的mse 可能没看懂是是坏，希望是越小越好，与目标（y_test）约靠近也就是预测越准确，下面换个算法--决策树看看
"""
1、加载与切分数据
"""
#加载数据
from sklearn.datasets import load_boston
#加载切分工具
from sklearn.model_selection import train_test_split
#切分数据
X,y = load_boston(return_X_y = True)
X_train,x_test,y_train,y_test = train_test_split(X,y,test_size =0.2)
"""
2、加载模型和预测
"""
#加载模型
from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor()
#训练
dtr.fit(X=X_train,y=y_train) #一开始想着训练模型会有多困难，结果就行几个的单词的代码
"""
3、预测值
"""
y_pred1 = dtr.predict(X=X_test)
"""
4、评估模型
"""
mse = ((y_pred1 - y_test)**2).mean()
mse
