79 lines
2.4 KiB
Python
79 lines
2.4 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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The figures show the confusion matrix with and without
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normalization by class support size (number of elements
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in each class). This kind of normalization can be
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interesting in case of class imbalance to have a more
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visual interpretation of which class is being misclassified.
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Here the results are not as good as they could be as our
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choice for the regularization parameter C was not the best.
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In real life applications this parameter is usually chosen
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using :ref:`grid_search`.
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"""
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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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 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, using a model that is too regularized (C too low) to see
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# the impact on the results
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classifier = svm.SVC(kernel='linear', C=0.01)
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y_pred = classifier.fit(X_train, y_train).predict(X_test)
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def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
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plt.imshow(cm, interpolation='nearest', cmap=cmap)
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plt.title(title)
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plt.colorbar()
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tick_marks = np.arange(len(iris.target_names))
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plt.xticks(tick_marks, iris.target_names, rotation=45)
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plt.yticks(tick_marks, iris.target_names)
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plt.tight_layout()
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plt.ylabel('True label')
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plt.xlabel('Predicted label')
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# Compute confusion matrix
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cm = confusion_matrix(y_test, y_pred)
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np.set_printoptions(precision=2)
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print('Confusion matrix, without normalization')
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print(cm)
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plt.figure()
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plot_confusion_matrix(cm)
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# Normalize the confusion matrix by row (i.e by the number of samples
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# in each class)
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cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
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print('Normalized confusion matrix')
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print(cm_normalized)
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plt.figure()
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plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
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plt.show()
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