e650a207efc9dd33556b1b9678b043f73a18aecb,examples/model_selection/plot_confusion_matrix.py,,,#,27

Before Change

// Run classifier, using a model that is too regularized (C too low) to see
// the impact on the results
classifier = svm.SVC(kernel="linear", C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)


def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,

After Change

np.set_printoptions(precision=2)

// Plot non-normalized confusion matrix
titles_options = [("Confusion matrix, without normalization", None),
                  ("Normalized confusion matrix", "true")]
for title, normalize in titles_options:
    disp = plot_confusion_matrix(classifier, X_test, y_test,
                                 display_labels=class_names,
                                 cmap=plt.cm.Blues,
                                 normalize=normalize)
    disp.ax_.set_title(title)

    print(title)
    print(disp.confusion_matrix)

plt.show()

Instances