  Since the publication of Breiman (2001), Random Forests (RF) have been widely
used in both regression and classification. Later on, other forests are also
proposed and studied in literature and Mondrian Forests are notable examples
built on the Mondrian process; see Lakshminarayanan et al. (2014). In this
paper, we propose an ensemble estimator in general statistical learning based
on Mondrian Forests, which can be regarded as an extension of RF. This general
framework includes many common learning problems, such as least squared
regression, least $\ell_1$ regression, quantile regression and classification.
Under mild conditions of loss functions, we give the upper bound of the regret
function of our estimator and show that such estimator is statistically
consistent.
