混淆矩阵的构造 [英] Construction of confusion matrix

问题描述

我对以下链接中的混淆矩阵的构造有疑问:

I have a question concerning the construction of confusion matrix from the below link: Ranger Predicted Class Probability of each row in a data frame

例如，如果我有以下代码(如链接中的答案所解释):

If I have the following code for example (as explained by the answer in the link):

library(ranger)
library(caret)

idx = sample(nrow(iris),100)
data = iris
data$Species = factor(ifelse(data$Species=="versicolor",1,0))
Train_Set = data[idx,]
Test_Set = data[-idx,]

mdl <- ranger(Species ~ ., ,data=Train_Set,importance="impurity", save.memory = TRUE, probability=TRUE)
probabilities <- as.data.frame(predict(mdl, data = Test_Set,type='response', verbose = TRUE)$predictions)
max.col(probabilities) - 1

调用

confusionMatrix(table(Test_Set$Species, max.col(probabilities)-1))

收益:

然后，使用此

caret::confusionMatrix(table(max.col(probabilities) - 1,Test_Set$Species))

提供

哪种方法是创建混淆矩阵的正确方法，因为灵敏度，特异性，ppv，npv的值因tp，tn，fp，fn开关的不同而不同?

Which is the right way to create confusion matrix, since the values of sensitivity, specificity, ppv, npv differs becuase tp, tn, fp, fn switches?

如果我要求将正类设为1而不是使用

If I demand the positive class to be 1 rather using

caret::confusionMatrix(table(max.col(probabilities) - 1,Test_Set$Species), positive = '1')

我得到

那么，矩阵中的值是tp = 13，tn = 36，fp = 0，fn = 1，对吗?

So, the values in the matrices are tp = 13, tn = 36, fp = 0, fn = 1, correct?

我对如何读取混淆矩阵的值感到困惑.

I am confused as to how to read the values of the confusion matrix.

推荐答案

我了解混淆矩阵的构造，以及如果更改了类，则条目的作用.

I have understood the construction of confusion matrices and the role of the entries if the class is changed.

使用以下方法获得的0类的混淆矩阵

The confusion matrices for the class 0 obtained using

caret::confusionMatrix(table(max.col(probabilities) - 1,Test_Set$Species), positive = '0')

以及使用

caret::confusionMatrix(table(max.col(probabilities) - 1,Test_Set$Species), positive = '1')

和

对于第0类，tp = 36，tn = 13，fp = 1，fn = 0，对于第1类:tp = 13，tn = 36，fp = 0，fn = 1(角色tp和tn的值，以及fp和fn的值被切换).

In case of class 0: tp = 36, tn = 13, fp = 1, fn = 0, and in case of class 1: tp = 13, tn = 36, fp = 0, fn = 1 (the roles of tp and tn, and that of fp and fn are switched).

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