.. _example_model_selection_plot_roc.py: ======================================= Receiver Operating Characteristic (ROC) ======================================= Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. This means that the top left corner of the plot is the "ideal" point - a false positive rate of zero, and a true positive rate of one. This is not very realistic, but it does mean that a larger area under the curve (AUC) is usually better. The "steepness" of ROC curves is also important, since it is ideal to maximize the true positive rate while minimizing the false positive rate. Multiclass settings ------------------- ROC curves are typically used in binary classification to study the output of a classifier. In order to extend ROC curve and ROC area to multi-class or multi-label classification, it is necessary to binarize the output. One ROC curve can be drawn per label, but one can also draw a ROC curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging). Another evaluation measure for multi-class classification is macro-averaging, which gives equal weight to the classification of each label. .. note:: See also :func:`sklearn.metrics.roc_auc_score`, :ref:`example_model_selection_plot_roc_crossval.py`. .. rst-class:: horizontal * .. image:: images/plot_roc_001.png :scale: 47 * .. image:: images/plot_roc_002.png :scale: 47 **Python source code:** :download:`plot_roc.py ` .. literalinclude:: plot_roc.py :lines: 38- **Total running time of the example:** 0.31 seconds ( 0 minutes 0.31 seconds)