本实例展示怎样使用cross_val_predict来可视化预测错误:

# coding:utf-8

from pylab import *

from sklearn import datasets

from sklearn.model_selection import cross_val_predict

from sklearn import linear_model

lr = linear_model.LinearRegression()

boston = datasets.load_boston()

y = boston.target

#cross_val_predict返回和`y`相同尺寸的数组

#每一个entry是通过交叉验证的相应预测

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

#设置中文字体

myfont = matplotlib.font_manager.FontProperties(fname="Microsoft-Yahei-UI-Light.ttc")

mpl.rcParams['axes.unicode_minus'] = False

#绘制

plt.scatter(y,predicted)

plt.plot([y.min(),y.max()],[y.min(),y.max()],"k--",lw=4)

plt.title(u'绘制交叉验证预测',fontproperties=myfont)

plt.xlabel(u'测度',fontproperties=myfont)

plt.ylabel(u'预测',fontproperties=myfont)

#显示绘制结果

plt.show()