如何在多类别分类模型中准确提取混淆矩阵中的假阳性和假阴性指标

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()


完成此操作并给出混淆矩阵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class0来说是什么意思：它表示所有被正确识别为
_not0_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

True
True
True
True
True
True
True
True
True
True