.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_cv_predict.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model_selection_plot_cv_predict.py: ==================================== Plotting Cross-Validated Predictions ==================================== This example shows how to use :func:`~sklearn.model_selection.cross_val_predict` together with :class:`~sklearn.metrics.PredictionErrorDisplay` to visualize prediction errors. .. GENERATED FROM PYTHON SOURCE LINES 13-15 We will load the diabetes dataset and create an instance of a linear regression model. .. GENERATED FROM PYTHON SOURCE LINES 15-21 .. code-block:: Python from sklearn.datasets import load_diabetes from sklearn.linear_model import LinearRegression X, y = load_diabetes(return_X_y=True) lr = LinearRegression() .. GENERATED FROM PYTHON SOURCE LINES 22-25 :func:`~sklearn.model_selection.cross_val_predict` returns an array of the same size of `y` where each entry is a prediction obtained by cross validation. .. GENERATED FROM PYTHON SOURCE LINES 25-29 .. code-block:: Python from sklearn.model_selection import cross_val_predict y_pred = cross_val_predict(lr, X, y, cv=10) .. GENERATED FROM PYTHON SOURCE LINES 30-39 Since `cv=10`, it means that we trained 10 models and each model was used to predict on one of the 10 folds. We can now use the :class:`~sklearn.metrics.PredictionErrorDisplay` to visualize the prediction errors. On the left axis, we plot the observed values :math:`y` vs. the predicted values :math:`\hat{y}` given by the models. On the right axis, we plot the residuals (i.e. the difference between the observed values and the predicted values) vs. the predicted values. .. GENERATED FROM PYTHON SOURCE LINES 39-66 .. code-block:: Python import matplotlib.pyplot as plt from sklearn.metrics import PredictionErrorDisplay fig, axs = plt.subplots(ncols=2, figsize=(8, 4)) PredictionErrorDisplay.from_predictions( y, y_pred=y_pred, kind="actual_vs_predicted", subsample=100, ax=axs[0], random_state=0, ) axs[0].set_title("Actual vs. Predicted values") PredictionErrorDisplay.from_predictions( y, y_pred=y_pred, kind="residual_vs_predicted", subsample=100, ax=axs[1], random_state=0, ) axs[1].set_title("Residuals vs. Predicted Values") fig.suptitle("Plotting cross-validated predictions") plt.tight_layout() plt.show() .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_predict_001.png :alt: Plotting cross-validated predictions, Actual vs. Predicted values, Residuals vs. Predicted Values :srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_predict_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 67-80 It is important to note that we used :func:`~sklearn.model_selection.cross_val_predict` for visualization purpose only in this example. It would be problematic to quantitatively assess the model performance by computing a single performance metric from the concatenated predictions returned by :func:`~sklearn.model_selection.cross_val_predict` when the different CV folds vary by size and distributions. It is recommended to compute per-fold performance metrics using: :func:`~sklearn.model_selection.cross_val_score` or :func:`~sklearn.model_selection.cross_validate` instead. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.184 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_cv_predict.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/model_selection/plot_cv_predict.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_cv_predict.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_cv_predict.py ` .. include:: plot_cv_predict.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_