72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
"""
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=======================================
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Receiver Operating Characteristic (ROC)
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=======================================
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Example of Receiver Operating Characteristic (ROC) metric to evaluate
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classifier output quality.
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ROC curves typically feature true positive rate on the Y axis, and false
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positive rate on the X axis. This means that the top left corner of the plot is
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the "ideal" point - a false positive rate of zero, and a true positive rate of
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one. This is not very realistic, but it does mean that a larger area under the
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curve (AUC) is usually better.
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The "steepness" of ROC curves is also important, since it is ideal to maximize
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the true positive rate while minimizing the false positive rate.
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.. note::
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See also :func:`sklearn.metrics.auc_score`,
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:ref:`example_plot_roc_crossval.py`
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"""
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print(__doc__)
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import numpy as np
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import pylab as pl
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from sklearn import svm, datasets
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from sklearn.utils import shuffle
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from sklearn.metrics import roc_curve, auc
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from sklearn.cross_validation import train_test_split
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# Import some data to play with
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# Make it a binary classification problem by removing the third class
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X, y = X[y != 2], y[y != 2]
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# Add noisy features to make the problem harder
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random_state = np.random.RandomState(0)
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n_samples, n_features = X.shape
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X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
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# shuffle and split training and test sets
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=.5, random_state=0)
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# Run classifier
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classifier = svm.SVC(kernel='linear', probability=True,
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random_state=random_state)
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probas_ = classifier.fit(X_train, y_train).predict_proba(X_test)
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# Compute ROC curve and area the curve
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fpr, tpr, thresholds = roc_curve(y_test, probas_[:, 1])
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roc_auc = auc(fpr, tpr)
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print("Area under the ROC curve : %f" % roc_auc)
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# Plot ROC curve
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pl.clf()
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pl.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
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pl.plot([0, 1], [0, 1], 'k--')
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pl.xlim([0.0, 1.0])
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pl.ylim([0.0, 1.0])
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pl.xlabel('False Positive Rate')
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pl.ylabel('True Positive Rate')
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pl.title('Receiver operating characteristic example')
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pl.legend(loc="lower right")
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pl.show()
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