Caret包 - 定义阳性结果

Question

使用Caret软件包进行机器学习时,我对Caret默认的"正面"结果选择感到震惊,即二元分类问题中结果因素的第一级.

包裹说它可以设置为替代级别.任何人都能帮助我确定积极的结果吗？

感谢您

Answer 1

看看这个例子.使用confusionMatrix从插入符号示例扩展了这一点.

lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
                levels = rev(lvs))
pred <- factor(
  c(
    rep(lvs, times = c(54, 32)),
    rep(lvs, times = c(27, 231))),               
  levels = rev(lvs))

xtab <- table(pred, truth)

str(truth)
Factor w/ 2 levels "abnormal","normal": 2 2 2 2 2 2 2 2 2 2 ...

因为异常是第一级,这将是默认的正类

confusionMatrix(xtab)

Confusion Matrix and Statistics

          truth
pred       abnormal normal
  abnormal      231     32
  normal         27     54

               Accuracy : 0.8285          
                 95% CI : (0.7844, 0.8668)
    No Information Rate : 0.75            
    P-Value [Acc > NIR] : 0.0003097       

                  Kappa : 0.5336          
 Mcnemar's Test P-Value : 0.6025370       

            Sensitivity : 0.8953          
            Specificity : 0.6279          
         Pos Pred Value : 0.8783          
         Neg Pred Value : 0.6667          
             Prevalence : 0.7500          
         Detection Rate : 0.6715          
   Detection Prevalence : 0.7645          
      Balanced Accuracy : 0.7616          

       'Positive' Class : abnormal     

要更改为正类=正常,只需在confusionMatrix中添加它.注意与先前输出的差异,差异开始出现在灵敏度和其他计算中.

confusionMatrix(xtab, positive = "normal")

Confusion Matrix and Statistics

          truth
pred       abnormal normal
  abnormal      231     32
  normal         27     54

               Accuracy : 0.8285          
                 95% CI : (0.7844, 0.8668)
    No Information Rate : 0.75            
    P-Value [Acc > NIR] : 0.0003097       

                  Kappa : 0.5336          
 Mcnemar's Test P-Value : 0.6025370       

            Sensitivity : 0.6279          
            Specificity : 0.8953          
         Pos Pred Value : 0.6667          
         Neg Pred Value : 0.8783          
             Prevalence : 0.2500          
         Detection Rate : 0.1570          
   Detection Prevalence : 0.2355          
      Balanced Accuracy : 0.7616          

       'Positive' Class : normal