.. _example_model_selection_plot_confusion_matrix.py: ================ Confusion matrix ================ Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions. The figures show the confusion matrix with and without normalization by class support size (number of elements in each class). This kind of normalization can be interesting in case of class imbalance to have a more visual interpretation of which class is being misclassified. Here the results are not as good as they could be as our choice for the regularization parameter C was not the best. In real life applications this parameter is usually chosen using :ref:`grid_search`. .. rst-class:: horizontal * .. image:: images/plot_confusion_matrix_001.png :scale: 47 * .. image:: images/plot_confusion_matrix_002.png :scale: 47 **Script output**:: Confusion matrix, without normalization [[13 0 0] [ 0 10 6] [ 0 0 9]] Normalized confusion matrix [[ 1. 0. 0. ] [ 0. 0.62 0.38] [ 0. 0. 1. ]] **Python source code:** :download:`plot_confusion_matrix.py ` .. literalinclude:: plot_confusion_matrix.py :lines: 26- **Total running time of the example:** 0.44 seconds ( 0 minutes 0.44 seconds)