[英]Is it possible to retrieve false positive and false negative from confusion matrix in R?
I have generated a confusion matrix using R as follows. 我使用R生成了一个混淆矩阵,如下所示。
Is it possible to retrieve the false negative value of 61 from this matrix and assign to a variable in R? 是否可以从该矩阵中检索假负值61并分配给R中的变量? $byClass does't seem to work for this case.
$ byClass似乎不适用于这种情况。 Thanks.
谢谢。
Confusion Matrix and Statistics
Reference
Prediction no yes
no 9889 61
yes 6 44
Accuracy : 0.9933
95% CI : (0.9915, 0.9948)
No Information Rate : 0.9895
P-Value [Acc > NIR] : 4.444e-05
Kappa : 0.5648
Mcnemar's Test P-Value : 4.191e-11
Sensitivity : 0.9994
Specificity : 0.4190
Pos Pred Value : 0.9939
Neg Pred Value : 0.8800
Prevalence : 0.9895
Detection Rate : 0.9889
Detection Prevalence : 0.9950
Balanced Accuracy : 0.7092
'Positive' Class : no
You haven't provided a reproducible example or loaded any packages in your code, but it looks like you're using confusionMatrix
from the caret
package. 您尚未提供可复制的示例,也未在代码中加载任何包,但似乎您使用的是
caret
包中的confusionMatrix
。 Here's a generic example: 这是一个通用示例:
library(caret)
# Fake data
dat = data.frame(measured=rep(0:1, c(40,60)), modeled=rep(c(0:1,0:1), c(30,10,20,40)))
# Generate confusion matrix
cm = confusionMatrix(dat$modeled, dat$measured, positive="1")
cm
Confusion Matrix and Statistics Reference Prediction 0 1 0 30 20 1 10 40 Accuracy : 0.7 95% CI : (0.6002, 0.7876) No Information Rate : 0.6 P-Value [Acc > NIR] : 0.02478 Kappa : 0.4 Mcnemar's Test P-Value : 0.10035 Sensitivity : 0.6667 Specificity : 0.7500 Pos Pred Value : 0.8000 Neg Pred Value : 0.6000 Prevalence : 0.6000 Detection Rate : 0.4000 Detection Prevalence : 0.5000 Balanced Accuracy : 0.7083 'Positive' Class : 1
cm
is actually a list, so let's see what it contains: cm
实际上是一个列表,所以让我们看看它包含的内容:
str(cm)
List of 6
$ positive: chr "1"
$ table : 'table' int [1:2, 1:2] 30 10 20 40
..- attr(*, "dimnames")=List of 2
.. ..$ Prediction: chr [1:2] "0" "1"
.. ..$ Reference : chr [1:2] "0" "1"
$ overall : Named num [1:7] 0.7 0.4 0.6 0.788 0.6 ...
..- attr(*, "names")= chr [1:7] "Accuracy" "Kappa" "AccuracyLower" "AccuracyUpper" ...
$ byClass : Named num [1:11] 0.667 0.75 0.8 0.6 0.8 ...
..- attr(*, "names")= chr [1:11] "Sensitivity" "Specificity" "Pos Pred Value" "Neg Pred Value" ...
$ mode : chr "sens_spec"
$ dots : list()
- attr(*, "class")= chr "confusionMatrix"
It looks like cm$table
has the actual confusion matrix: 看起来
cm$table
具有实际的混淆矩阵:
cm$table
Reference Prediction 0 1 0 30 20 1 10 40
So the count of false positives is: 因此,误报的计数为:
cm$table[2,1]
[1] 10
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