.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_confusion_matrix.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_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`. .. GENERATED FROM PYTHON SOURCE LINES 26-65 .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/model_selection/images/sphx_glr_plot_confusion_matrix_001.png :alt: Confusion matrix, without normalization :class: sphx-glr-multi-img * .. image:: /auto_examples/model_selection/images/sphx_glr_plot_confusion_matrix_002.png :alt: Normalized confusion matrix :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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. ]] | .. code-block:: default print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.model_selection import train_test_split from sklearn.metrics import plot_confusion_matrix # import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target class_names = iris.target_names # Split the data into a training set and a test set X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # 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).fit(X_train, y_train) 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.308 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_confusion_matrix.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_confusion_matrix.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_confusion_matrix.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_confusion_matrix.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_