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`

?

**Answer**

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.

**tl;dr**

`confusionMatrix`

uses the predicted classes and thus a 50% probability cutoff

Max

**Attribution***Source : Link , Question Author : Black Milk , Answer Author : Dan Villarreal*