regr.randomForest: regression using randomForest.
Description
a mlr learner for regrssion tasks using randomForest.Details
if predict.type = "se" the se.method (by default “jackknife”)
is estimated, using the methods described in Sexton and Laake (2009). If se.method = "bootstrap" the standard error of a prediction is estimated by bootstrapping the random forest, where the number of bootstrap replicates and the number of trees in the ensemble are controlled by se.boot and ntree.for.se respectively, and then taking the standard deviation of the predictions. If se.method = "jackknife", the default, the standard error of a prediction is estimated by computing the jackknife-after-bootstrap, the mean-squared difference between the prediction made by only using trees which did not contain said observation and the ensemble prediction. For both “jackknife” and “bootstrap”, a Monte-Carlo bias correction is applied and, in the case that this results in a negative variance estimate, the values are truncated at 0.References
[Joseph Sexton] and [Petter Laake],; [Standard errors for bagged and random forest estimators], Computational Statistics and Data Analysis Volume 53, 2009, [801-811].