MERCS: Multi-Directional Ensembles of Regression and Classification treeS
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](https://dtai.cs.kuleuven.be/).
Easy via pip;
` pip install mercs `
Our (very small) website can be found [here](https://eliavw.github.io/mercs/).
Cf. the [quickstart section](https://eliavw.github.io/mercs/quickstart) of the website.
MERCS is fully open-source cf. our [github-repository](https://github.com/eliavw/mercs/)
MERCS is an active research project, hence we periodically publish our findings;
### MERCS: Multi-Directional Ensembles of Regression and Classification Trees
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](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16875/16735)
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