Some learners like random forest use bagging. Bagging means that the learner consists of an ensemble of several base learners and each base learner is trained with a different random subsample or bootstrap sample from all observations. A prediction made for an observation in the original data set using only base learners not trained on this particular observation is called out-of-bag (OOB) prediction. These predictions are not prone to overfitting, as each prediction is only made by learners that did not use the observation for training.

To get a list of learners that provide OOB predictions, you can call listLearners(obj = NA, properties = "oobpreds").

listLearners(obj = NA, properties = "oobpreds")[c("class", "package")]
##                     class         package
## 1    classif.randomForest    randomForest
## 2 classif.randomForestSRC randomForestSRC
## 3          classif.ranger          ranger
## 4          classif.rFerns          rFerns
## 5       regr.randomForest    randomForest
## 6    regr.randomForestSRC randomForestSRC
## ... (#rows: 8, #cols: 2)

In mlr function getOOBPreds() can be used to extract these observations from the trained models. These predictions can be used to evaluate the performance of a given learner like in the following example.

lrn = makeLearner("classif.ranger", predict.type = "prob", predict.threshold = 0.6)
mod = train(lrn, sonar.task)
oob = getOOBPreds(mod, sonar.task)
oob
## Prediction: 208 observations
## predict.type: prob
## threshold: M=0.60,R=0.40
## time: NA
##   id truth    prob.M    prob.R response
## 1  1     R 0.5331047 0.4668953        R
## 2  2     R 0.6056414 0.3943586        M
## 3  3     R 0.5798999 0.4201001        R
## 4  4     R 0.4472660 0.5527340        R
## 5  5     R 0.6056473 0.3943527        M
## 6  6     R 0.4417684 0.5582316        R
## ... (#rows: 208, #cols: 5)

performance(oob, measures = list(auc, mmce))
##       auc      mmce 
## 0.9238414 0.2067308

As the predictions that are used are out-of-bag, this evaluation strategy is very similar to common resampling strategies like 10-fold cross-validation, but much faster, as only one training instance of the model is required.