a649429a334e01a964e75065c812bce4391c0f5d,plots.py,,plot_roc_curve,#Any#Any#,373

Before Change

            plt.plot(fpr, tpr, lw=1, label="ROC Fold %d (area = %0.2f)" % (i, roc_auc))
        // plot mean ROC
        plt.plot([0, 1], [0, 1], "--", color=(0.6, 0.6, 0.6), label="Luck")
        mean_tpr /= len(cv)
        mean_tpr[-1] = 1.0
        mean_auc = auc(mean_fpr, mean_tpr)
        plt.plot(mean_fpr, mean_tpr, "k--",
                 label="Mean ROC (area = %0.2f)" % mean_auc, lw=2)

After Change

        mean_tpr = 0.0
        mean_fpr = np.linspace(0, 1, 100)
        plt.figure()
        colors = cycle(["cyan", "indigo", "seagreen", "yellow", "blue", "darkorange"])
        lw = 2
        // cross-validation
        i = 0
        for (train, test), color in zip(cv.split(X, y), colors):

Instances