.. _sphx_glr_auto_examples_model_selection_grid_search_digits.py:


============================================================
Parameter estimation using grid search with cross-validation
============================================================

This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.model_selection.GridSearchCV` object
on a development set that comprises only half of the available labeled data.

The performance of the selected hyper-parameters and trained model is
then measured on a dedicated evaluation set that was not used during
the model selection step.

More details on tools available for model selection can be found in the
sections on :ref:`cross_validation` and :ref:`grid_search`.



.. code-block:: python


    from __future__ import print_function

    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import GridSearchCV
    from sklearn.metrics import classification_report
    from sklearn.svm import SVC

    print(__doc__)

    # Loading the Digits dataset
    digits = datasets.load_digits()

    # To apply an classifier on this data, we need to flatten the image, to
    # turn the data in a (samples, feature) matrix:
    n_samples = len(digits.images)
    X = digits.images.reshape((n_samples, -1))
    y = digits.target

    # Split the dataset in two equal parts
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.5, random_state=0)

    # Set the parameters by cross-validation
    tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                         'C': [1, 10, 100, 1000]},
                        {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

    scores = ['precision', 'recall']

    for score in scores:
        print("# Tuning hyper-parameters for %s" % score)
        print()

        clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5,
                           scoring='%s_macro' % score)
        clf.fit(X_train, y_train)

        print("Best parameters set found on development set:")
        print()
        print(clf.best_params_)
        print()
        print("Grid scores on development set:")
        print()
        means = clf.cv_results_['mean_test_score']
        stds = clf.cv_results_['std_test_score']
        for mean, std, params in zip(means, stds, clf.cv_results_['params']):
            print("%0.3f (+/-%0.03f) for %r"
                  % (mean, std * 2, params))
        print()

        print("Detailed classification report:")
        print()
        print("The model is trained on the full development set.")
        print("The scores are computed on the full evaluation set.")
        print()
        y_true, y_pred = y_test, clf.predict(X_test)
        print(classification_report(y_true, y_pred))
        print()

    # Note the problem is too easy: the hyperparameter plateau is too flat and the
    # output model is the same for precision and recall with ties in quality.

**Total running time of the script:**
(0 minutes 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`grid_search_digits.py <grid_search_digits.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`grid_search_digits.ipynb <grid_search_digits.ipynb>`