confusionMatrix - 错误：`data` 和 `reference` 应该是具有相同水平的因素

Question

创建了一个物流 model：

Banks_Logit<- glm(Banks$Financial.Condition ~ .,data = Banks, family="binomial")
options(scipen=999)

summary(Banks_Logit)

然后：

pred <-predict(Banks_Logit,Banks)
gain <-gains(Banks$Financial.Condition,pred,groups=20)

plot(c(0,gain$cume.pct.of.total*sum(Banks$Financial.Condition))~
   c(0,gain$cume.obs), 
 xlab = "Observations", ylab = "Cumulative", main="Model Performance", type="l")
      lines(c(0,sum(Banks$Financial.Condition))~c(0,dim(Banks)[1]),lty=2)

library(caret)
confusionMatrix(ifelse(pred >0.5, 1,0), Banks$Financial.Condition)

error - 错误： data和reference应该是具有相同水平的因素。

这是预测数据

        1                        2                        3                        4                        5                        6                        7                        8                        9                       10 
0.9999999999999997779554 0.9999999999999997779554 0.9999999999999997779554 0.9999999999999997779554 0.9999999999999997779554 0.9999999999999997779554 0.9999999999138624584560 0.9999999999891036051025 0.9999999999995110577800 0.9999999999999997779554 
                      11                       12                       13                       14                       15                       16                       17                       18                       19                       20 
0.0000000000176082421301 0.0000000000352379135751 0.0000000000431425778626 0.0000000000000002220446 0.0000000000002227450487 0.0000000000000002220446 0.0000000000000002220446 0.0000000000000002220446 0.0000000000000002220446 0.0000000000000002220446 



str(pred)
 Named num [1:20] 1 1 1 1 1 ...
 - attr(*, "names")= chr [1:20] "1" "2" "3" "4" ...



this is the dataset (Str(Banks):
'data.frame':   20 obs. of  5 variables:
 $ Obs                : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Financial.Condition: int  1 1 1 1 1 1 1 1 1 1 ...
 $ TotCap.Assets      : num  9.7 1 6.9 5.8 4.3 9.1 11.9 8.1 9.3 1.1 ...
 $ TotExp.Assets      : num  0.12 0.11 0.09 0.1 0.11 0.13 0.1 0.13 0.16 0.16 ...
 $ TotLns.Lses.Assets : num  0.65 0.62 1.02 0.67 0.69 0.74 0.79 0.63 0.72 0.57 ...

Answer 1

一些示例数据：

Banks = data.frame(Obs = 1:100,Financial.Condition=rbinom(100,1,0.5),
TotCap.Assets = runif(100),
TotExp.Assets = runif(100),TotLns.Lses.Assets = runif(100))

您可以只提供confusionMatrix的表格，以获取其他指标：

library(caret)

Banks_Logit<- glm(Banks$Financial.Condition ~ .,data = Banks, family="binomial")
pred <-predict(Banks_Logit,Banks)
confusionMatrix(table(ifelse(pred >0.5, 1,0), Banks$Financial.Condition))

Confusion Matrix and Statistics

   
     0  1
  0 36 33
  1  8 23
                                          
               Accuracy : 0.59            
                 95% CI : (0.4871, 0.6874)
    No Information Rate : 0.56            
    P-Value [Acc > NIR] : 0.3084356       
                                          
                  Kappa : 0.2158          
                                          
 Mcnemar's Test P-Value : 0.0001781       
                                          
            Sensitivity : 0.8182          
            Specificity : 0.4107          
         Pos Pred Value : 0.5217          
         Neg Pred Value : 0.7419          
             Prevalence : 0.4400          
         Detection Rate : 0.3600          
   Detection Prevalence : 0.6900          
      Balanced Accuracy : 0.6144          
                                          
       'Positive' Class : 0