用中文解释This is a MATLAB function that takes in a confusion matrix and a boolean variable called 'verbatim'. The confusion matrix is a 3x3 matrix that is the output of the MATLAB function 'confusionmat'. The function then computes various statistics based on this confusion matrix and returns them in a struct called 'stats'. The statistics computed are true positives (tp), false positives (fp), false negatives (fn), and true negatives (tn). These statistics are calculated for each class in the confusion matrix. The function loops through each class and computes the corresponding statistics. The 'verbose' variable controls whether the generated table is output to the command window. If 'verbose' is set to 1, the table is output. Otherwise, it is not. Overall, this function is useful for evaluating the performance of a classification algorithm by analyzing the confusion matrix.

这是一个MATLAB函数，它接受一个混淆矩阵和一个名为'verbose'的布尔变量作为输入。混淆矩阵是一个3x3矩阵，是MATLAB函数'confusionmat'的输出。该函数基于混淆矩阵计算各种统计数据，并将它们返回到名为'stats'的结构体中。计算的统计数据包括真正例(tp)、假正例(fp)、假负例(fn)和真负例(tn)。这些统计数据是针对混淆矩阵中的每个类别计算的。函数通过循环遍历每个类别并计算相应的统计数据。'verbose'变量控制生成的表格是否输出到命令窗口。如果'verbose'设置为1，则输出表格，否则不输出。总的来说，这个函数通过分析混淆矩阵，有助于评估分类算法的性能。

相关问题

2023/6/6 16:45:29 (193224) What is the Precision in percentage based on following confusion matrix of three class classification. Confusion Matrix C=[6 2] [1 3] A 55% B 75% C 86% D 45% 选择正确选项

根据所给的混淆矩阵C=[6 2] [1 3]，可以计算出每个类别的精度（precision）和召回率（recall）。

精度是指被分类器判定为某个类别的样本中，真正属于该类别的样本所占的比例。

召回率是指属于某个类别的样本中，被分类器正确地判定为该类别的样本所占的比例。

根据上述定义，可以计算出每个类别的精度和召回率如下：

类别 A：

精度 = TP_A / (TP_A + FP_A) = 6 / (6 + 1) ≈ 0.857

召回率 = TP_A / (TP_A + FN_A) = 6 / (6 + 2) = 0.75

类别 B：

精度 = TP_B / (TP_B + FP_B) = 3 / (3 + 2) = 0.6

召回率 = TP_B / (TP_B + FN_B) = 3 / (3 + 1) = 0.75

类别 C：

精度 = TP_C / (TP_C + FP_C) = 0 / (0 + 0) = undefined (由于没有被分类为类别 C 的样本，分母为0，精度为undefined)

召回率 = TP_C / (TP_C + FN_C) = 0 / (0 + 3) = 0

其中，TP_A 表示真正属于类别 A 的样本中，被分类器正确地判定为类别 A 的样本数；FP_A 表示被分类器错误地判定为类别 A 的样本中，实际上不属于类别 A 的样本数；FN_A 表示真正属于类别 A 的样本中，被分类器错误地判定为其他类别的样本数；同理，TP_B、FP_B、FN_B、TP_C、FP_C、FN_C 的含义也类似。

根据精度的定义，可以得出以下选项中哪一个是正确的：

C. 86%

分析下列实验结果：This is the experiment of Subject # 3 // The accuracy is 0.573469387755102 This is the experiment of Subject # 8 // The accuracy is 0.632070707070707 This is the experiment of Subject # 9 // The accuracy is 0.6675824175824177 This is the experiment of Subject # 10 // The accuracy is 0.8071428571428572 This is the experiment of Subject # 11 // The accuracy is 0.59652133580705 This is the experiment of Subject # 12 // The accuracy is 0.7999999999999999 This is the experiment of Subject # 13 // The accuracy is 0.8654761904761905 This is the experiment of Subject # 14 // The accuracy is 0.8313775510204081 This is the experiment of Subject # 15 // The accuracy is 0.6232323232323232 This is the experiment of Subject # 16 // The accuracy is 0.5939393939393939 The Overall WAR is 0.7271028037383177 The Overall UAR is 0.7146362879966881 The confusion matrix is [[108 0 1 1 17 0 0] [ 0 58 7 1 0 3 12] [ 4 2 35 2 0 1 2] [ 12 1 2 41 5 5 3] [ 21 0 1 9 40 0 0] [ 4 5 3 1 3 60 3] [ 0 12 3 0 0 0 47]] WAR of each subject: [0.69387755 0.65517241 0.72093023 0.81578947 0.65454545 0.82857143 0.85245902 0.79710145 0.64285714 0.66197183] UAR of each subject: [0.57346939 0.63207071 0.66758242 0.80714286 0.59652134 0.8 0.86547619 0.83137755 0.62323232 0.59393939]

这些实验结果是某个分类任务的结果。每个实验代表了一个受试者的结果，实验的准确率（accuracy）在结果中给出。最后给出了整体的准确率（WAR）和无加权的平均准确率（UAR）以及混淆矩阵。其中，WAR是所有受试者准确率的平均值，UAR是每个受试者的准确率的平均值。混淆矩阵则给出了分类器在每个类别上的分类结果，可以用于评估分类器的性能和找出分类器分类错误的原因。此外，还给出了每个受试者的WAR和UAR，用于评估每个受试者的分类性能。需要注意的是，WAR和UAR可能会受到类别不平衡的影响，需要根据具体情况进行评估。