<gh_stars>1-10
# -*- coding: utf-8 -*-

"""
@author: XuMing <<EMAIL>>
@summary:
"""
from sklearn import datasets
from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt


lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target

predicted = cross_val_predict(lr,boston.data,y,cv=10)

fig,ax = plt.subplots()
ax.scatter(y,predicted)
ax.plot([y.min(),y.max()],[y.min(),y.max()],'k--',lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()
# -*- coding: utf-8 -*-

"""
@author: XuMing <<EMAIL>>
@summary:
"""
from sklearn import datasets
from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt


lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target

predicted = cross_val_predict(lr,boston.data,y,cv=10)

fig,ax = plt.subplots()
ax.scatter(y,predicted)
ax.plot([y.min(),y.max()],[y.min(),y.max()],'k--',lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()
# -*- coding: utf-8 -*- @author: XuMing <<EMAIL>> @summary: