The confusion matrix contains results about the classification accuracy of the model. For a given tree in the forest, a class vote for a row in the out-of-bag data is the predicted class for the row from the single tree. The predicted class for a row in out-of-bag data is the class with the highest vote across all trees in the forest.


The count is the number of rows in the data.

True positive rate (sensitivity or power)

False positive rate (type I error)

False negative rate (type II error)

True negative rate (specificity)