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#https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html import numpy as np from sklearn import metrics import matplotlib.pyplot as plt #model #1 y = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]) scores = np.array([0.64, 0.47, 0.46, 0.77, 0.72, 0.9, 0.85, 0.7, 0.87, 0.92, 0.89, 0.93, 0.85, 0.81, 0.88, 0.48, 0.1, 0.35, 0.68, 0.47]) fpr, tpr, thresholds = metrics.roc_curve(y, scores) roc_auc = metrics.auc(fpr, tpr) # plot plt.title('Receiver Operating Characteristic') plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc) plt.legend(loc = 'lower right') plt.plot([0, 1], [0, 1],'r--') plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') plt.show()
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