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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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