plot_cv_predict.rst.txt

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    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_model_selection_plot_cv_predict.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

Plotting Cross-Validated Predictions

This example shows how to use :func:`~sklearn.model_selection.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_diabetes(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()

.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.247 seconds)

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 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


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  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_cv_predict.py <plot_cv_predict.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_cv_predict.ipynb <plot_cv_predict.ipynb>`

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