Question

我已经尝试使用玩具数据进行k-NN分类并获得如下预测：

actual <- c(rep('A1',12), rep('A2',12), rep('A3',7), rep('A4',12), rep('B1',11), rep('B2',17), rep('C1',15))
prediction <- c('A1','A1','A1','A1','A1','A3','A4','A4','B1','B2','C1','C1',
                'A2','A2','A2','A2','A2','A3','A4','A4','A4','B1','B1','C1',
                'A1','A2','A3','A3','A3','A3','B2',
                'A1','A1','A2','A2','A2','A4','A4','A4','A4','A4','A4','B1',
                'A1','A2','A2','A4','B1','B1','B1','B2','B2','B2','B2',
                'A1','A3','B1','B1','B1','B1','B2','B2','B2','B2','B2','B2','B2','B2','B2','C1','C1',
                'A1','A1','A2','B2','B2','C1','C1','C1','C1','C1','C1','C1','C1','C1','C1')

使用table() as：

可以实现预测的基本思路

table(actual, prediction)
#       prediction
# actual A1 A2 A3 A4 B1 B2 C1
#     A1  5  0  1  2  1  1  2
#     A2  0  5  1  3  2  0  1
#     A3  1  1  4  0  0  1  0
#     A4  2  3  0  6  1  0  0
#     B1  1  2  0  1  3  4  0
#     B2  1  0  1  0  4  9  2
#     C1  2  1  0  0  0  2 10

有一个信息丰富的功能caret::confusionMatrix()。

caret::confusionMatrix(prediction, actual)
# Confusion Matrix and Statistics
# 
# Reference
# Prediction A1 A2 A3 A4 B1 B2 C1
# A1  5  0  1  2  1  1  2
# A2  0  5  1  3  2  0  1
# A3  1  1  4  0  0  1  0
# A4  2  3  0  6  1  0  0
# B1  1  2  0  1  3  4  0
# B2  1  0  1  0  4  9  2
# C1  2  1  0  0  0  2 10
# 
# Overall Statistics
# 
# Accuracy : 0.4884         
# 95% CI : (0.379, 0.5986)
# No Information Rate : 0.1977         
# P-Value [Acc > NIR] : 1.437e-09      
# 
# Kappa : 0.3975         
# Mcnemar's Test P-Value : NA             
# 
# Statistics by Class:
# 
#                      Class: A1 Class: A2 Class: A3 Class: A4 Class: B1 Class: B2 Class: C1
# Sensitivity            0.41667   0.41667   0.57143   0.50000   0.27273    0.5294    0.6667
# Specificity            0.90541   0.90541   0.96203   0.91892   0.89333    0.8841    0.9296
# Pos Pred Value         0.41667   0.41667   0.57143   0.50000   0.27273    0.5294    0.6667
# Neg Pred Value         0.90541   0.90541   0.96203   0.91892   0.89333    0.8841    0.9296
# Prevalence             0.13953   0.13953   0.08140   0.13953   0.12791    0.1977    0.1744
# Detection Rate         0.05814   0.05814   0.04651   0.06977   0.03488    0.1047    0.1163
# Detection Prevalence   0.13953   0.13953   0.08140   0.13953   0.12791    0.1977    0.1744
# Balanced Accuracy      0.66104   0.66104   0.76673   0.70946   0.58303    0.7067    0.7981

我观察到有许多子类属于另一个类。例如，A1，A2，A3，A4属于班级A。同样地，B1，B2属于班级B。我想在将类中的所有子类视为相等之后计算统计数据。是否有任何函数可用于在类和类外错误中生成如上所述的类似统计信息？

注意：请不要提出包含从子类中删除数字的解决方案，因为实际应用程序与此类似。为简单起见，我给出了这个例子。

如果给出了类和子类关系，是否可以获得解决方案？

Answer 1

如何通过删除子类后缀手动定义类：

    actual <- c(rep('A1',12), rep('A2',12), rep('A3',7), rep('A4',12), rep('B1',11), rep('B2',17), rep('C1',15))
    prediction <- c('A1','A1','A1','A1','A1','A3','A4','A4','B1','B2','C1','C1',
                    'A2','A2','A2','A2','A2','A3','A4','A4','A4','B1','B1','C1',
                    'A1','A2','A3','A3','A3','A3','B2',
                    'A1','A1','A2','A2','A2','A4','A4','A4','A4','A4','A4','B1',
                    'A1','A2','A2','A4','B1','B1','B1','B2','B2','B2','B2',
                    'A1','A3','B1','B1','B1','B1','B2','B2','B2','B2','B2','B2','B2','B2','B2','C1','C1',
                    'A1','A1','A2','B2','B2','C1','C1','C1','C1','C1','C1','C1','C1','C1','C1')
    actual = gsub("\\d", "", actual)
    prediction = gsub("\\d", "", prediction)
    caret::confusionMatrix(prediction, actual)

#output
Confusion Matrix and Statistics

          Reference
Prediction  A  B  C
         A 34  6  3
         B  6 20  2
         C  3  2 10

Overall Statistics

               Accuracy : 0.7442          
                 95% CI : (0.6387, 0.8322)
    No Information Rate : 0.5             
    P-Value [Acc > NIR] : 3.272e-06       

                  Kappa : 0.5831          
 Mcnemar's Test P-Value : 1               

Statistics by Class:

                     Class: A Class: B Class: C
Sensitivity            0.7907   0.7143   0.6667
Specificity            0.7907   0.8621   0.9296
Pos Pred Value         0.7907   0.7143   0.6667
Neg Pred Value         0.7907   0.8621   0.9296
Prevalence             0.5000   0.3256   0.1744
Detection Rate         0.3953   0.2326   0.1163
Detection Prevalence   0.5000   0.3256   0.1744
Balanced Accuracy      0.7907   0.7882   0.7981

多类混淆矩阵，用于计算类内错误

1 个答案: