I've obtained a logistic regression model (via train) for a binary response, and I've obtained the logistic confusion matrix via confusionMatrix in caret. It gives me the logistic model confusion matrix, though I'm not sure what threshold is being used to obtain it. How do I obtain the confusion matrix for specific threshold values using confusionMatrix in caret?

  • $\begingroup$ I don't have an answer, but often questions like this are answered in the help file. If that fails, you can look at the source code itself. You can print the source to the console by typing confusionmatrix, without parentheses. $\endgroup$ – shadowtalker Aug 7 '14 at 1:36
  • $\begingroup$ It isn't quite clear what you have done exactly. Did you call the glm function from the stats package and pass its result to confusionMatrix? I didn't know one could do that, and reading the manual it isn't clear one can at all. Or did you predict something? A short example would help. $\endgroup$ – Calimo Aug 7 '14 at 6:33
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    $\begingroup$ @Calimo I've used the train function in caret to fit the model, which lets me specify it as a glm with binomial family. I then used the predict function on the object generated via train. $\endgroup$ – Black Milk Aug 7 '14 at 20:14

Most classification models in R produce both a class prediction and the probabilities for each class. For binary data, in almost every case, the class prediction is based on a 50% probability cutoff.

glm is the same. With caret, using predict(object, newdata) gives you the predicted class and predict(object, new data, type = "prob") will give you class-specific probabilities (when object is generated by train).

You can do things differently by defining your own model and applying whatever cutoff that you want. The caret website also has an example that uses resampling to optimize the probability cutoff.


confusionMatrix uses the predicted classes and thus a 50% probability cutoff


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There is a pretty easy way, assuming tune <- train(...):

probsTest <- predict(tune, test, type = "prob")
threshold <- 0.5
pred      <- factor( ifelse(probsTest[, "yes"] > threshold, "yes", "no") )
pred      <- relevel(pred, "yes")   # you may or may not need this; I did
confusionMatrix(pred, test$response)

Obviously, you can set threshold to whatever you want to try or pick the "best" one, where best means highest combined specificity and sensitivity:

probsTrain <- predict(tune, train, type = "prob")
rocCurve   <- roc(response = train$response,
                      predictor = probsTrain[, "yes"],
                      levels = rev(levels(train$response)))
plot(rocCurve, print.thres = "best")

After looking at the example Max posted, I'm not sure if there are some statistical nuances making my approach less desired.

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  • $\begingroup$ In the outputted rocCurve plot, what do the three values mean? e.g. on my data it says 0.289 (0.853, 0.831). Does the 0.289 signify the best threshold that one should use in demarcating the binary outcome? i.e. every case with a predicted probability > 0.289 would be coded "1" and every case with a predicted probability < 0.289 would be coded "0", rather than the 0.5 default threshold of the caret package? $\endgroup$ – coip Feb 15 '18 at 23:14
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    $\begingroup$ yep that's exactly right, and the other 2 values in parentheses are sensitivity and specificity (honestly, though, I forget which is which) $\endgroup$ – efh0888 Jul 31 '18 at 13:41
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    $\begingroup$ also, since then I figured out you can extract it from the roc curve using rocCurve$thresholds[which(rocCurve$sensitivities + rocCurve$specificities == max(rocCurve$sensitivities + rocCurve$specificities))] which also gives you the flexibility to weight them differently if you want... one last thing to note is that realistically, you probably want to tune the threshold (like you would with any model hyperparameter) as Max describes here. $\endgroup$ – efh0888 Jul 31 '18 at 13:47

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