.. _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 ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`grid_search_digits.ipynb `