Travis Heeter Travis Heeter - 10 months ago 50
R Question

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

I'm looking at this data set: 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="+")
#### ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ####

[1] "+"

testPred - +
- 99 6
+ 20 88

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

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

[1] "sens_spec"


[1] "confusionMatrix"

So Sensetivity is:

enter image description here (from caret's confusionMatrix doc)

If you take my confusion matrix:

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 Source

UPDATE: I found it's a lot better to send the data, rather than a table as a parameter. From the confusionMatrix docs:

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:

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:

testPred  -  +
       - 99  6
       + 20 88

Caret's Confusion Matrix:

testPred  -  +
       - 99  6
       + 20 88

Although now it says $positive: "-" so I'm not sure if that's good or bad.