How to send a confusion matrix to caret's confusionMatrix? - r

Question

I'm looking at this data set: https://archive.ics.uci.edu/ml/datasets/Credit+Approval. I built a ctree:
myFormula<-class~. # class is a factor of "+" or "-"
ct <- ctree(myFormula, data = train)
And now I'd like to put that data into caret's confusionMatrix method to get all the stats associated with the confusion matrix:
testPred <- predict(ct, newdata = test)
#### This is where I'm doing something wrong ####
confusionMatrix(table(testPred, test$class),positive="+")
#### ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ####
$positive
[1] "+"
$table
td
testPred - +
- 99 6
+ 20 88
$overall
Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue
8.779343e-01 7.562715e-01 8.262795e-01 9.186911e-01 5.586854e-01 6.426168e-24 1.078745e-02
$byClass
Sensitivity Specificity Pos Pred Value Neg Pred Value Precision Recall F1
0.9361702 0.8319328 0.8148148 0.9428571 0.8148148 0.9361702 0.8712871
Prevalence Detection Rate Detection Prevalence Balanced Accuracy
0.4413146 0.4131455 0.5070423 0.8840515
$mode
[1] "sens_spec"
$dots
list()
attr(,"class")
[1] "confusionMatrix"
So Sensetivity is:
(from caret's confusionMatrix doc)
If you take my confusion matrix:
$table
td
testPred - +
- 99 6
+ 20 88
You can see this doesn't add up: Sensetivity = 99/(99+20) = 99/119 = 0.831928. In my confusionMatrix results, that value is for Specificity. However Specificity is Specificity = D/(B+D) = 88/(88+6) = 88/94 = 0.9361702, the value for Sensitivity.
I've tried this confusionMatrix(td,testPred, positive="+") but got even weirder results. What am I doing wrong?
UPDATE: I also realized that my confusion matrix is different than what caret thought it was:
Mine: Caret:
td testPred
testPred - + td - +
- 99 6 - 99 20
+ 20 88 + 6 88
As you can see, it thinks my False Positive and False Negative are backwards.

Answer 1

UPDATE: I found it's a lot better to send the data, rather than a table as a parameter. From the confusionMatrix docs:
reference
a factor of classes to be used as the true results
I took this to mean what symbol constitutes a positive outcome. In my case, this would have been a +. However, 'reference' refers to the actual outcomes from the data set, aka the dependent variable.
So I should have used confusionMatrix(testPred, test$class). If your data is out of order for some reason, it will shift it into the correct order (so the positive and negative outcomes/predictions align correctly in the confusion matrix.
However, if you are worried about the outcome being the correct factor, install the plyr library, and use revalue to change the factor:
install.packages("plyr")
library(plyr)
newDF <- df
newDF$class <- revalue(newDF$class,c("+"=1,"-"=0))
# You'd have to rerun your model using newDF
I'm not sure why this worked, but I just removed the positive parameter:
confusionMatrix(table(testPred, test$class))
My Confusion Matrix:
td
testPred - +
- 99 6
+ 20 88
Caret's Confusion Matrix:
td
testPred - +
- 99 6
+ 20 88
Although now it says $positive: "-" so I'm not sure if that's good or bad.