机器学习sklearn（76）：算法实例（三十三）回归（五）线性回归大家族（三）回归类的模型评估指标

from sklearn.metrics import mean_squared_error as MSE
MSE(yhat,Ytest) y.max()
y.min()
cross_val_score(reg,X,y,cv=10,scoring="mean_squared_error") #为什么报错了？来试试看！
import sklearn
sorted(sklearn.metrics.SCORERS.keys())
cross_val_score(reg,X,y,cv=10,scoring="neg_mean_squared_error")

#调用R2
from sklearn.metrics import r2_score
r2_score(yhat,Ytest)
r2 = reg.score(Xtest,Ytest)
r2

#使用shift tab键来检查究竟哪个值先进行输入
r2_score(Ytest,yhat) #或者你也可以指定参数，就不必在意顺序了
r2_score(y_true = Ytest,y_pred = yhat)
cross_val_score(reg,X,y,cv=10,scoring="r2").mean()

import matplotlib.pyplot as plt
sorted(Ytest)
plt.plot(range(len(Ytest)),sorted(Ytest),c="black",label= "Data")
plt.plot(range(len(yhat)),sorted(yhat),c="red",label = "Predict")
plt.legend()
plt.show()

import numpy as np
rng = np.random.RandomState(42) X = rng.randn(100, 80) y = rng.randn(100)
cross_val_score(LR(), X, y, cv=5, scoring=‘r2‘)