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File Content: `plot_cv_predict.py`

"""
====================================
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()
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)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()

Name	Size	Permission
applications	---	0755
bicluster	---	0755
calibration	---	0755
classification	---	0755
cluster	---	0755
covariance	---	0755
cross_decomposition	---	0755
datasets	---	0755
decomposition	---	0755
ensemble	---	0755
exercises	---	0755
feature_selection	---	0755
gaussian_process	---	0755
linear_model	---	0755
manifold	---	0755
mixture	---	0755
model_selection	---	0755
neighbors	---	0755
neural_networks	---	0755
preprocessing	---	0755
semi_supervised	---	0755
svm	---	0755
text	---	0755
tree	---	0755
README.txt	116 bytes	0644
feature_stacker.py	1911 bytes	0644
hetero_feature_union.py	6241 bytes	0644
missing_values.py	3055 bytes	0644
plot_compare_reduction.py	2489 bytes	0644
plot_cv_predict.py	799 bytes	0644
plot_digits_pipe.py	1813 bytes	0644
plot_isotonic_regression.py	1767 bytes	0644
plot_johnson_lindenstrauss_bound.py	7474 bytes	0644
plot_kernel_approximation.py	8004 bytes	0644
plot_kernel_ridge_regression.py	6269 bytes	0644
plot_multilabel.py	4157 bytes	0644
plot_multioutput_face_completion.py	3019 bytes	0644

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Action

applications

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0755