69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
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"""
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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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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets, svm
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from sklearn.metrics import ConfusionMatrixDisplay
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from sklearn.model_selection 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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class_names = iris.target_names
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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).fit(X_train, y_train)
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np.set_printoptions(precision=2)
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# Plot non-normalized confusion matrix
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titles_options = [
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("Confusion matrix, without normalization", None),
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("Normalized confusion matrix", "true"),
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]
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for title, normalize in titles_options:
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disp = ConfusionMatrixDisplay.from_estimator(
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classifier,
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X_test,
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y_test,
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display_labels=class_names,
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cmap=plt.cm.Blues,
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normalize=normalize,
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)
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disp.ax_.set_title(title)
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print(title)
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print(disp.confusion_matrix)
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plt.show()
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