Learners like randomForest produce out-of-bag predictions.
getOOBPreds extracts this information from trained models and builds a
prediction object as provided by predict (with prediction time set to NA).
In the classification case:
What is stored exactly in the (Prediction) object depends
on the predict.type setting of the Learner.
You can call listLearners(properties = "oobpreds") to get a list of learners
which provide this.
Arguments
- model
(WrappedModel)
The model.- task
(Task)
The task.
Value
(Prediction).
Examples
training.set = sample(1:150, 50)
lrn = makeLearner("classif.ranger", predict.type = "prob", predict.threshold = 0.6)
mod = train(lrn, sonar.task, subset = training.set)
#> Error in x[0, , drop = FALSE]: incorrect number of dimensions
oob = getOOBPreds(mod, sonar.task)
#> Error in checkClass(x, classes, ordered, null.ok): object 'mod' not found
oob
#> Error in eval(expr, envir, enclos): object 'oob' not found
performance(oob, measures = list(auc, mmce))
#> Error in performance(oob, measures = list(auc, mmce)): object 'oob' not found