from sklearn import datasets
from sklearn.cross_validation 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

# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validated:
# 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()

print datasets.load_boston()
