We propose a random forest autoencoder with applications in tabular, image and genomic data.

The introduced TreeHFD algorithm estimates the Hoeffding or ANOVA functional decomposition of tree ensembles from a data sample, and therefore provides an efficient explainability method through a set of low dimensional functions.