Calibrating random forests for probability estimation.

ABSTRACT: Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illu

SUBMITTER: Dankowski T 

PROVIDER: S-EPMC5074325 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature