In [1]:

    

from sklearn import datasets
from sklearn.cross_validation import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt


    

In [2]:

    

lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target
# boston.data[506]*13


    

In [3]:

    

# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validated:
# cv:A cross-validation generator to use. If int, determines the number of folds in StratifiedKFold.
predicted = cross_val_predict(lr, boston.data, y, cv=10)


    

In [9]:

    

fig,ax = plt.subplots()


    

In [10]:

    

ax.scatter(y, predicted)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'r--', lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
fig.show()


    

In [11]:

    

plt.show()