.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_roc_crossval.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_roc_crossval.py: ============================================================= Receiver Operating Characteristic (ROC) with cross validation ============================================================= This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation. ROC curves typically feature true positive rate (TPR) on the Y axis, and false positive rate (FPR) on the X axis. This means that the top left corner of the plot is the "ideal" point - a FPR of zero, and a TPR 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 TPR while minimizing the FPR. This example shows the ROC response of different datasets, created from K-fold cross-validation. Taking all of these curves, it is possible to calculate the mean AUC, and see the variance of the curve when the training set is split into different subsets. This roughly shows how the classifier output is affected by changes in the training data, and how different the splits generated by K-fold cross-validation are from one another. .. note:: See :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` for a complement of the present example explaining the averaging strategies to generalize the metrics for multiclass classifiers. .. GENERATED FROM PYTHON SOURCE LINES 31-42 Load and prepare data ===================== We import the :ref:`iris_dataset` which contains 3 classes, each one corresponding to a type of iris plant. One class is linearly separable from the other 2; the latter are **not** linearly separable from each other. In the following we binarize the dataset by dropping the "virginica" class (`class_id=2`). This means that the "versicolor" class (`class_id=1`) is regarded as the positive class and "setosa" as the negative class (`class_id=0`). .. GENERATED FROM PYTHON SOURCE LINES 42-52 .. code-block:: default import numpy as np from sklearn.datasets import load_iris iris = load_iris() target_names = iris.target_names X, y = iris.data, iris.target X, y = X[y != 2], y[y != 2] n_samples, n_features = X.shape .. GENERATED FROM PYTHON SOURCE LINES 53-54 We also add noisy features to make the problem harder. .. GENERATED FROM PYTHON SOURCE LINES 54-57 .. code-block:: default random_state = np.random.RandomState(0) X = np.concatenate([X, random_state.randn(n_samples, 200 * n_features)], axis=1) .. GENERATED FROM PYTHON SOURCE LINES 58-65 Classification and ROC analysis ------------------------------- Here we run a :class:`~sklearn.svm.SVC` classifier with cross-validation and plot the ROC curves fold-wise. Notice that the baseline to define the chance level (dashed ROC curve) is a classifier that would always predict the most frequent class. .. GENERATED FROM PYTHON SOURCE LINES 65-133 .. code-block:: default import matplotlib.pyplot as plt from sklearn import svm from sklearn.metrics import auc from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import StratifiedKFold cv = StratifiedKFold(n_splits=6) classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state) tprs = [] aucs = [] mean_fpr = np.linspace(0, 1, 100) fig, ax = plt.subplots(figsize=(6, 6)) for fold, (train, test) in enumerate(cv.split(X, y)): classifier.fit(X[train], y[train]) viz = RocCurveDisplay.from_estimator( classifier, X[test], y[test], name=f"ROC fold {fold}", alpha=0.3, lw=1, ax=ax, ) interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr) interp_tpr[0] = 0.0 tprs.append(interp_tpr) aucs.append(viz.roc_auc) ax.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)") mean_tpr = np.mean(tprs, axis=0) mean_tpr[-1] = 1.0 mean_auc = auc(mean_fpr, mean_tpr) std_auc = np.std(aucs) ax.plot( mean_fpr, mean_tpr, color="b", label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc), lw=2, alpha=0.8, ) std_tpr = np.std(tprs, axis=0) tprs_upper = np.minimum(mean_tpr + std_tpr, 1) tprs_lower = np.maximum(mean_tpr - std_tpr, 0) ax.fill_between( mean_fpr, tprs_lower, tprs_upper, color="grey", alpha=0.2, label=r"$\pm$ 1 std. dev.", ) ax.set( xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], xlabel="False Positive Rate", ylabel="True Positive Rate", title=f"Mean ROC curve with variability\n(Positive label '{target_names[1]}')", ) ax.axis("square") ax.legend(loc="lower right") plt.show() .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_roc_crossval_001.png :alt: Mean ROC curve with variability (Positive label 'versicolor') :srcset: /auto_examples/model_selection/images/sphx_glr_plot_roc_crossval_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.168 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_roc_crossval.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.2.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_roc_crossval.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_roc_crossval.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_