regr.randomForest: RandomForest regression learner.

Description References

Description

mlr learner for regression tasks using randomForest.

This doc page exists, as we added additional uncertainty estimation functionality (predict.type = "se") for the randomForest, which is not provided by the underlying package.

Currently implemented methods are:

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.

Note that when using the “jackknife” procedure for se estimation, using a small number of trees can lead to training data observations that are never out-of-bag. The current implementation ignores these observations, but in the original definition, the resulting se estimation would be undefined.

Please note that all of the mentioned se.method variants do not affect the computation of the posterior mean “response” value. This is always the same as from the underlying randomForest.

References

[Joseph Sexton] and [Petter Laake]; [Standard errors for bagged and random forest estimators], Computational Statistics and Data Analysis Volume 53, 2009, [801-811]. Also see: [Stefan Wager], [Trevor Hastie], and [Bradley Efron]; [Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife], Journal of Machine Learning Research Volume 15, 2014, [1625-1651].


guillermozbta/mir documentation built on May 11, 2019, 6:27 p.m.