label='ROC curve ofclass{0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('FalsePositive Rate') plt.ylabel('TruePositive Rate') plt.title('Some extension of Receiver ...
plt.plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('T...
label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate')plt.ylabel('True Positive Rate') plt.title('Some extension of Recei...
importnumpy as npimportmatplotlib.pyplot as pltfromitertoolsimportcyclefromsklearnimportsvm, datasetsfromsklearn.metricsimportroc_curve, aucfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportlabel_binarizefromsklearn.multiclassimportOneVsRestClassifierfromscipyimportinterp#导入鸢尾花数...
plt.title('ROC curve') plt.legend(loc='best') plt.show() # Zoom in view of the upper left corner. plt.figure(2) plt.xlim(0, 0.2) plt.ylim(0.8, 1) plt.plot([0, 1], [0, 1], 'k--') plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras)...
importmatplotlib.pyplotasplt# 绘制多分类问题的ROC曲线plt.figure()foriinrange(n_classes):plt.plot(fpr[i],tpr[i],label='ROC curve of class {0} (area = {1:0.2f})'.format(i,roc_auc[i]))plt.plot([0,1],[0,1],'k--')# 绘制对角线plt.xlim([0.0,1.0])plt.ylim([0.0,1.05])plt...
label='ROC curve of class {0} (area = {1:0.2f})'''.format(i, roc_auc[i])) plt.plot([0,1], [0,1],'k--', lw=lw) plt.xlim([0.0,1.0]) plt.ylim([0.0,1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') ...
label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') ...
plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Some extension of ...
plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive ...