Note
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Plotting Cross-Validated Predictions¶
This example shows how to use
cross_val_predict to visualize prediction
errors.
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()
X, y = datasets.load_boston(return_X_y=True)
# 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, X, 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()
Total running time of the script: ( 0 minutes 0.482 seconds)
Estimated memory usage: 8 MB
