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.
Easy via pip:
pip install mercs-mixed
All the documentation and a quickstart guide can be accessed here: https://systemallica.github.io/mercs/
To run the project, you need Poetry. Once installed:
- Clone the repository.
- Run
poetry install. - The development environment is ready. You can test it by running
pytest.
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:
- Elia Van Wolputte
- Evgeniya Korneva
- Prof. Hendrik Blockeel
- Andrés Reverón Molina