如何从多类分类的混淆矩阵中提取假阳性，假阴性

如何从多类分类的混淆矩阵中提取假阳性，假阴性

首先，您的代码中有遗漏-为了运行，我需要添加以下命令：

import keras(x_train, y_train), (x_test, y_test) = mnist.load_data()

完成此 *** 作并给出混淆矩阵

cm1

：

array([[ 965,    0,    1,    0,    0,    2,    6,    1,    5,    0],       [   0, 1113,    4,    2,    0,    0,    3,    0,   13,    0],       [   8,    0,  963,   14,    5,    1,    7,    8,   21,    5],       [   0,    0,    3,  978,    0,    7,    0,    6,   12,    4],       [   1,    0,    4,    0,  922,    0,    9,    3,    3,   40],       [   4,    1,    1,   27,    0,  824,    6,    1,   20,    8],       [  11,    3,    1,    1,    5,    6,  925,    0,    6,    0],       [   2,    6,   17,    8,    2,    0,    1,  961,    2,   29],       [   5,    1,    2,   13,    4,    6,    2,    6,  929,    6],       [   6,    5,    0,    7,    5,    6,    1,    6,   10,  963]])

这是您如何获取 每类 要求的TP，FP，FN，TN的方法：

真实肯定只是对角线元素：

TruePositive = np.diag(cm1)TruePositive# array([ 965, 1113,  963,  978,  922,  824,  925,  961,  929,  963])

误报是各列的总和，减去对角线元素：

FalsePositive = []for i in range(num_classes):    FalsePositive.append(sum(cm1[:,i]) - cm1[i,i])FalsePositive# [37, 16, 33, 72, 21, 28, 35, 31, 92, 92]

同样，False Negatives是相应行的总和，减去对角线元素：

FalseNegative = []for i in range(num_classes):    FalseNegative.append(sum(cm1[i,:]) - cm1[i,i])FalseNegative# [15, 22, 69, 32, 60, 68, 33, 67, 45, 46]

现在，“真否定论”有些棘手；让我们首先考虑一个真正的否定词，相对于class

0

来说是什么意思：它表示所有被正确识别为
_not

0

_的样本。因此，本质上我们应该做的是从混淆矩阵中删除相应的行和列，然后对所有剩余元素求和：

TrueNegative = []for i in range(num_classes):    temp = np.delete(cm1, i, 0)   # delete ith row    temp = np.delete(temp, i, 1)  # delete ith column    TrueNegative.append(sum(sum(temp)))TrueNegative# [8998, 8871, 9004, 8950, 9057, 9148, 9040, 9008, 8979, 8945]

让我们进行完整性检查：对于 每个类 ，TP，FP，FN和TN的总和必须等于测试集的大小（此处为10,000）：让我们确认确实如此：

l = len(y_test)for i in range(num_classes):    print(TruePositive[i] + FalsePositive[i] + FalseNegative[i] + TrueNegative[i] == l)

结果是

TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue