  Decision trees and their ensembles are very popular models of supervised
machine learning. In this paper we merge the ideas underlying decision trees,
their ensembles and FCA by proposing a new supervised machine learning model
which can be constructed in polynomial time and is applicable for both
classification and regression problems. Specifically, we first propose a
polynomial-time algorithm for constructing a part of the concept lattice that
is based on a decision tree. Second, we describe a prediction scheme based on a
concept lattice for solving both classification and regression tasks with
prediction quality comparable to that of state-of-the-art models.
