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.. 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 <sklearn.model_selection.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
<sklearn.model_selection.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.
     <http://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf>`_

.. GENERATED FROM PYTHON SOURCE LINES 46-128



.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_nested_cross_validation_iris_001.png
   :alt: Non-Nested and Nested Cross Validation on Iris Dataset
   :srcset: /auto_examples/model_selection/images/sphx_glr_plot_nested_cross_validation_iris_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Average difference of 0.007581 with std. dev. of 0.007833.






|

.. code-block:: default


    import numpy as np
    from matplotlib import pyplot as plt

    from sklearn.datasets import load_iris
    from sklearn.model_selection import GridSearchCV, KFold, cross_val_score
    from sklearn.svm import SVC

    # 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": [0.01, 0.1]}

    # 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=outer_cv)
        clf.fit(X_iris, y_iris)
        non_nested_scores[i] = clf.best_score_

        # Nested CV with parameter optimization
        clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)
        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, 0.4, 0.5, 0),
    )
    plt.title(
        "Non-Nested and Nested Cross Validation on Iris Dataset",
        x=0.5,
        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, 0.8, 0),
    )
    plt.ylabel("score difference", fontsize="14")

    plt.show()


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

   **Total running time of the script:** (0 minutes 3.692 seconds)


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