插入符号包 - 定义正结果 [英] Caret package - defining Positive result

问题描述

在使用 Caret 包进行机器学习时，我对 Caret 的默认积极"结果选择感到震惊，即二元分类问题中结果因子的第一级.

While using Caret package for machine learning, I am struck with Caret's default "Positive" outcome picking i.e the first level of the outcome factor in binary classification problems.

Package 说它可以设置为替代级别.任何机构都可以帮助我定义积极的结果吗?

Package says it can be set to the alternative level. Can any body help me to define the positive outcome?

谢谢

推荐答案

看看这个例子.使用混淆矩阵从插入符号示例中扩展了这一点.

look at this example. Extended this from the caret examples with confusionMatrix.

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

Because abnormal is the first level, this will be the default positive class

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，只需将其添加到混淆矩阵中即可.注意与之前输出的差异，差异开始出现在灵敏度和其他计算中.

To change to positive class = normal, just add this in the confusionMatrix. Notice the differences with the previous output, differences start appearing at the sensitivity and other calculations.

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 

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