Created
February 20, 2019 16:53
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import scikit-learn | |
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 | |
# 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() |
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