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Parameter estimation using grid search with a nested cross-validation

The classifier is optimized by “nested” cross-validation using the sklearn.grid_search.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 Cross-Validation and Grid Search.

Python source code:

print __doc__

from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.svm import SVC

# 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 =

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_fraction=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', precision_score),
    ('recall', recall_score),

for score_name, score_func in scores:
    print "# Tuning hyper-parameters for %s" % score_name

    clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func), y_train, cv=5)

    print "Best parameters set found on development set:"
    print clf.best_estimator_
    print "Grid scores on development set:"
    for params, mean_score, scores in clf.grid_scores_:
        print "%0.3f (+/-%0.03f) for %r" % (
            mean_score, scores.std() / 2, params)

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

# 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.