.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py: ========================================= Nested versus non-nested cross-validation ========================================= This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estimates the generalization error of the underlying model and its (hyper)parameter search. Choosing the parameters that maximize non-nested CV biases the model to the dataset, yielding an overly-optimistic score. Model selection without nested CV uses the same data to tune model parameters and evaluate model performance. Information may thus "leak" into the model and overfit the data. The magnitude of this effect is primarily dependent on the size of the dataset and the stability of the model. See Cawley and Talbot [1]_ for an analysis of these issues. To avoid this problem, nested CV effectively uses a series of train/validation/test set splits. In the inner loop (here executed by :class:`GridSearchCV `), the score is approximately maximized by fitting a model to each training set, and then directly maximized in selecting (hyper)parameters over the validation set. In the outer loop (here in :func:`cross_val_score `), generalization error is estimated by averaging test set scores over several dataset splits. The example below uses a support vector classifier with a non-linear kernel to build a model with optimized hyperparameters by grid search. We compare the performance of non-nested and nested CV strategies by taking the difference between their scores. .. topic:: See Also: - :ref:`cross_validation` - :ref:`grid_search` .. topic:: References: .. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res 2010,11, 2079-2107. `_ .. image:: /auto_examples/model_selection/images/sphx_glr_plot_nested_cross_validation_iris_001.png :alt: Non-Nested and Nested Cross Validation on Iris Dataset :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Average difference of 0.007581 with std. dev. of 0.007833. | .. code-block:: default from sklearn.datasets import load_iris from matplotlib import pyplot as plt from sklearn.svm import SVC from sklearn.model_selection import GridSearchCV, cross_val_score, KFold import numpy as np print(__doc__) # Number of random trials NUM_TRIALS = 30 # Load the dataset iris = load_iris() X_iris = iris.data y_iris = iris.target # Set up possible values of parameters to optimize over p_grid = {"C": [1, 10, 100], "gamma": [.01, .1]} # We will use a Support Vector Classifier with "rbf" kernel svm = SVC(kernel="rbf") # Arrays to store scores non_nested_scores = np.zeros(NUM_TRIALS) nested_scores = np.zeros(NUM_TRIALS) # Loop for each trial for i in range(NUM_TRIALS): # Choose cross-validation techniques for the inner and outer loops, # independently of the dataset. # E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc. inner_cv = KFold(n_splits=4, shuffle=True, random_state=i) outer_cv = KFold(n_splits=4, shuffle=True, random_state=i) # Non_nested parameter search and scoring clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv) clf.fit(X_iris, y_iris) non_nested_scores[i] = clf.best_score_ # Nested CV with parameter optimization nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv) nested_scores[i] = nested_score.mean() score_difference = non_nested_scores - nested_scores print("Average difference of {:6f} with std. dev. of {:6f}." .format(score_difference.mean(), score_difference.std())) # Plot scores on each trial for nested and non-nested CV plt.figure() plt.subplot(211) non_nested_scores_line, = plt.plot(non_nested_scores, color='r') nested_line, = plt.plot(nested_scores, color='b') plt.ylabel("score", fontsize="14") plt.legend([non_nested_scores_line, nested_line], ["Non-Nested CV", "Nested CV"], bbox_to_anchor=(0, .4, .5, 0)) plt.title("Non-Nested and Nested Cross Validation on Iris Dataset", x=.5, y=1.1, fontsize="15") # Plot bar chart of the difference. plt.subplot(212) difference_plot = plt.bar(range(NUM_TRIALS), score_difference) plt.xlabel("Individual Trial #") plt.legend([difference_plot], ["Non-Nested CV - Nested CV Score"], bbox_to_anchor=(0, 1, .8, 0)) plt.ylabel("score difference", fontsize="14") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 4.652 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_nested_cross_validation_iris.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nested_cross_validation_iris.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nested_cross_validation_iris.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_