47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
"""
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================
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Confusion matrix
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================
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Example of confusion matrix usage to evaluate the quality
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of the output of a classifier on the iris data set. The
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diagonal elements represent the number of points for which
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the predicted label is equal to the true label, while
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off-diagonal elements are those that are mislabeled by the
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classifier. The higher the diagonal values of the confusion
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matrix the better, indicating many correct predictions.
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"""
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print(__doc__)
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from sklearn import svm, datasets
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from sklearn.cross_validation import train_test_split
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from sklearn.metrics import confusion_matrix
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import pylab as pl
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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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# Split the data into a training set and a test set
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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# Run classifier
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classifier = svm.SVC(kernel='linear')
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y_pred = classifier.fit(X_train, y_train).predict(X_test)
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# Compute confusion matrix
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cm = confusion_matrix(y_test, y_pred)
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print(cm)
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# Show confusion matrix in a separate window
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pl.matshow(cm)
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pl.title('Confusion matrix')
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pl.colorbar()
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pl.ylabel('True label')
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pl.xlabel('Predicted label')
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pl.show()
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