Transformation Forests

Understand

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.