  Regression models for supervised learning problems with a continuous target
are commonly understood as models for the conditional mean of the target given
predictors. This notion is simple and therefore appealing for interpretation
and visualisation. Information about the whole underlying conditional
distribution is, however, not available from these models. A more general
understanding of regression models as models for conditional distributions
allows much broader inference from such models, for example the computation of
prediction intervals. Several random forest-type algorithms aim at estimating
conditional distributions, most prominently quantile regression forests
(Meinshausen, 2006, JMLR). We propose a novel approach based on a parametric
family of distributions characterised by their transformation function. A
dedicated novel "transformation tree" algorithm able to detect distributional
changes is developed. Based on these transformation trees, we introduce
"transformation forests" as an adaptive local likelihood estimator of
conditional distribution functions. The resulting models are fully parametric
yet very general and allow broad inference procedures, such as the model-based
bootstrap, to be applied in a straightforward way.

Methodology;Machine Learning