Out-of-Bag Predictions
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
#> ... (8 rows, 2 cols)
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 0.5981283 0.4018717 R
#> 2 2 R 0.5493678 0.4506322 R
#> 3 3 R 0.5972328 0.4027672 R
#> 4 4 R 0.5151079 0.4848921 R
#> 5 5 R 0.5572582 0.4427418 R
#> 6 6 R 0.4191686 0.5808314 R
#> ... (208 rows, 5 cols)
performance(oob, measures = list(auc, mmce))
#> auc mmce
#> 0.9350794 0.1682692
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.