混淆矩阵、准确率、召回率、精准率、ROC曲线计算和可视化

用Python计算图像对不同地物分类精确度， 计算分类结果图与标准参考图的混淆矩阵， 对分类结果进行评价

混淆矩阵的MATLAB源代码，直接能用的代码，可以用来计算分类结果的准确率还能度量划分效果，混淆矩阵的概念虽然简单，但需要注意的点特别多，看此代码，绝对复制过去就能用！

Matlab code for computing and visualization: Confusion Matrix, Precision/Recall, ROC, Accuracy, F-Measure etc. for Classification. Matlab通过分类的label计算混淆矩阵Confusion Matrix并且显示的函数只要一句代码就行了，方便。 [confusion_matrix]=compute_confusion_matrix(predict_label,num_in_class,name_class);

基于MATLAB的KAPPA系数计算。The confusion matrix was used to describe the classification accuracy and to characterize the errors. Landis and Koch (1977) classified Kappa values into different categories: 0-0.20, 0.21-0.40, 0.41-0.60, 0.61-0.80 and 0.81-1, which indicates slight, fair, moderate, substantial and almost perfect agreements, respectively.

分类结果画混淆矩阵 采用matlab实现。