from sklearn import datasets
from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt
if __name__ == '__main__':
    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 validation:
    predicted = cross_val_predict(lr, boston.data, y, cv=10)

    fig, ax = plt.subplots()
    ax.scatter(y, predicted, edgecolors=(0, 0, 0))
    ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
    ax.set_xlabel('Measured')
    ax.set_ylabel('Predicted')
    plt.show()
from sklearn import datasets
from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt
if __name__ == '__main__':
    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 validation:
    predicted = cross_val_predict(lr, boston.data, y, cv=10)

    fig, ax = plt.subplots()
    ax.scatter(y, predicted, edgecolors=(0, 0, 0))
    ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
    ax.set_xlabel('Measured')
    ax.set_ylabel('Predicted')
    plt.show()
# cross_val_predict returns an array of the same size as `y` where each entry # is a prediction obtained by cross validation: