.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/exercises/plot_cv_diabetes.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_exercises_plot_cv_diabetes.py: =============================================== Cross-validation on diabetes Dataset Exercise =============================================== A tutorial exercise which uses cross-validation with linear models. This exercise is used in the :ref:`cv_estimators_tut` part of the :ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`. .. GENERATED FROM PYTHON SOURCE LINES 14-16 Load dataset and apply GridSearchCV ----------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 16-38 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from sklearn import datasets from sklearn.linear_model import Lasso from sklearn.model_selection import GridSearchCV X, y = datasets.load_diabetes(return_X_y=True) X = X[:150] y = y[:150] lasso = Lasso(random_state=0, max_iter=10000) alphas = np.logspace(-4, -0.5, 30) tuned_parameters = [{"alpha": alphas}] n_folds = 5 clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False) clf.fit(X, y) scores = clf.cv_results_["mean_test_score"] scores_std = clf.cv_results_["std_test_score"] .. GENERATED FROM PYTHON SOURCE LINES 39-41 Plot error lines showing +/- std. errors of the scores ------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 41-58 .. code-block:: Python plt.figure().set_size_inches(8, 6) plt.semilogx(alphas, scores) std_error = scores_std / np.sqrt(n_folds) plt.semilogx(alphas, scores + std_error, "b--") plt.semilogx(alphas, scores - std_error, "b--") # alpha=0.2 controls the translucency of the fill color plt.fill_between(alphas, scores + std_error, scores - std_error, alpha=0.2) plt.ylabel("CV score +/- std error") plt.xlabel("alpha") plt.axhline(np.max(scores), linestyle="--", color=".5") plt.xlim([alphas[0], alphas[-1]]) .. image-sg:: /auto_examples/exercises/images/sphx_glr_plot_cv_diabetes_001.png :alt: plot cv diabetes :srcset: /auto_examples/exercises/images/sphx_glr_plot_cv_diabetes_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (9.999999999999999e-05, 0.31622776601683794) .. GENERATED FROM PYTHON SOURCE LINES 59-61 Bonus: how much can you trust the selection of alpha? ----------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 61-91 .. code-block:: Python # To answer this question we use the LassoCV object that sets its alpha # parameter automatically from the data by internal cross-validation (i.e. it # performs cross-validation on the training data it receives). # We use external cross-validation to see how much the automatically obtained # alphas differ across different cross-validation folds. from sklearn.linear_model import LassoCV from sklearn.model_selection import KFold lasso_cv = LassoCV(alphas=alphas, random_state=0, max_iter=10000) k_fold = KFold(3) print("Answer to the bonus question:", "how much can you trust the selection of alpha?") print() print("Alpha parameters maximising the generalization score on different") print("subsets of the data:") for k, (train, test) in enumerate(k_fold.split(X, y)): lasso_cv.fit(X[train], y[train]) print( "[fold {0}] alpha: {1:.5f}, score: {2:.5f}".format( k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test]) ) ) print() print("Answer: Not very much since we obtained different alphas for different") print("subsets of the data and moreover, the scores for these alphas differ") print("quite substantially.") plt.show() .. rst-class:: sphx-glr-script-out .. code-block:: none Answer to the bonus question: how much can you trust the selection of alpha? Alpha parameters maximising the generalization score on different subsets of the data: [fold 0] alpha: 0.05968, score: 0.54209 [fold 1] alpha: 0.04520, score: 0.15521 [fold 2] alpha: 0.07880, score: 0.45192 Answer: Not very much since we obtained different alphas for different subsets of the data and moreover, the scores for these alphas differ quite substantially. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.516 seconds) .. _sphx_glr_download_auto_examples_exercises_plot_cv_diabetes.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/exercises/plot_cv_diabetes.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/exercises/plot_cv_diabetes.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_cv_diabetes.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_cv_diabetes.py ` .. include:: plot_cv_diabetes.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_