MERCS stands for multi-directional ensembles of classification and regression trees. It is a novel ML-paradigm under active development at the DTAI-lab at KU Leuven.
We offer a small collection of tutorials in the form of Jupyter Notebooks (cf. github-repo for the actual .ipynb files) of quick walkthroughs MERCS’ most common functionalities. These are intended as the most user-friendly entry point to our system.
MERCS 101
Our documentation can be found at read the docs
MERCS is fully open-source cf. our github-repository
MERCS is an active research project, hence we periodically publish our findings;
Abstract Learning a function f(X) that predicts Y from X is the archetypal Machine Learning (ML) problem. Typically, both sets of attributes (i.e., X,Y) have to be known before a model can be trained. When this is not the case, or when functions f(X) that predict Y from X are needed for varying X and Y, this may introduce significant overhead (separate learning runs for each function). In this paper, we explore the possibility of omitting the specification of X and Y at training time altogether, by learning a multi-directional, or versatile model, which will allow prediction of any Y from any X. Specifically, we introduce a decision tree-based paradigm that generalizes the well-known Random Forests approach to allow for multi-directionality. The result of these efforts is a novel method called MERCS: Multi-directional Ensembles of Regression and Classification treeS. Experiments show the viability of the approach.
Authors Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel
Open Access A pdf version can be found at AAAI-publications
People involved in this project: