.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_train_error_vs_test_error.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_train_error_vs_test_error.py: ========================= Train error vs Test error ========================= Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases the performance on train decreases while the performance on test is optimal within a range of values of the regularization parameter. The example with an Elastic-Net regression model and the performance is measured using the explained variance a.k.a. R^2. .. GENERATED FROM PYTHON SOURCE LINES 14-18 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 19-21 Generate sample data -------------------- .. GENERATED FROM PYTHON SOURCE LINES 21-39 .. code-block:: Python import numpy as np from sklearn import linear_model from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split n_samples_train, n_samples_test, n_features = 75, 150, 500 X, y, coef = make_regression( n_samples=n_samples_train + n_samples_test, n_features=n_features, n_informative=50, shuffle=False, noise=1.0, coef=True, ) X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=n_samples_train, test_size=n_samples_test, shuffle=False ) .. GENERATED FROM PYTHON SOURCE LINES 40-42 Compute train and test errors ----------------------------- .. GENERATED FROM PYTHON SOURCE LINES 42-60 .. code-block:: Python alphas = np.logspace(-5, 1, 60) enet = linear_model.ElasticNet(l1_ratio=0.7, max_iter=10000) train_errors = list() test_errors = list() for alpha in alphas: enet.set_params(alpha=alpha) enet.fit(X_train, y_train) train_errors.append(enet.score(X_train, y_train)) test_errors.append(enet.score(X_test, y_test)) i_alpha_optim = np.argmax(test_errors) alpha_optim = alphas[i_alpha_optim] print("Optimal regularization parameter : %s" % alpha_optim) # Estimate the coef_ on full data with optimal regularization parameter enet.set_params(alpha=alpha_optim) coef_ = enet.fit(X, y).coef_ .. rst-class:: sphx-glr-script-out .. code-block:: none Optimal regularization parameter : 0.00026529484644318975 .. GENERATED FROM PYTHON SOURCE LINES 61-63 Plot results functions ---------------------- .. GENERATED FROM PYTHON SOURCE LINES 63-89 .. code-block:: Python import matplotlib.pyplot as plt plt.subplot(2, 1, 1) plt.semilogx(alphas, train_errors, label="Train") plt.semilogx(alphas, test_errors, label="Test") plt.vlines( alpha_optim, plt.ylim()[0], np.max(test_errors), color="k", linewidth=3, label="Optimum on test", ) plt.legend(loc="lower right") plt.ylim([0, 1.2]) plt.xlabel("Regularization parameter") plt.ylabel("Performance") # Show estimated coef_ vs true coef plt.subplot(2, 1, 2) plt.plot(coef, label="True coef") plt.plot(coef_, label="Estimated coef") plt.legend() plt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.26) plt.show() .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_train_error_vs_test_error_001.png :alt: plot train error vs test error :srcset: /auto_examples/model_selection/images/sphx_glr_plot_train_error_vs_test_error_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.602 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_train_error_vs_test_error.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/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/model_selection/plot_train_error_vs_test_error.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_train_error_vs_test_error.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_train_error_vs_test_error.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_train_error_vs_test_error.zip ` .. include:: plot_train_error_vs_test_error.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_