如何根据python中的均值和标准差绘制ROC曲线？

Question

How to plot a figure like the photo based on 5 diferent ROC values and mean, and standard deviation are computed from thoes 5 ROC values? ROC curve

Answer 1

Here is an example from the sklearn website where you need to

• Classification is used for validation of the model.

• Separate training and testing dataset

• Apply CrossValidation on models

   import numpy as np from sklearn import datasets # Import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target X, y = X[y != 2], y[y != 2] n_samples, n_features = X.shape # Add noisy features random_state = np.random.RandomState(0) X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] 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 # Run classifier with cross-validation and plot ROC curves 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() for i, (train, test) in enumerate(cv.split(X, y)): classifier.fit(X[train], y[train]) viz = RocCurveDisplay.from_estimator( classifier, X[test], y[test], name="ROC fold {}".format(i), 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], linestyle="--", lw=2, color="r", label="Chance", alpha=0.8) 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], title="Receiver operating characteristic example", ) ax.legend(loc="lower right") plt.show()