Metadata-Version: 2.1
Name: mercs
Version: 0.0.43
Summary: MERCS: Multi-Directional Ensembles of Regression and Classification treeS
Home-page: https://github.com/eliavw
Author: Elia vw
Author-email: elia.vw@gmail.com
License: mit
Project-URL: Documentation, https://github.com/eliavw
Description: # MERCS
        
        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/).
        
        ## Installation
        
        Easy via pip;
        
        ```
        pip install mercs
        ```
        
        ## Website
        
        Our (very small) website can be found [here](https://eliavw.github.io/mercs/).
        
        
        ## Tutorials
        
        Cf. the [quickstart section](https://eliavw.github.io/mercs/quickstart) of the website.
        
        ## Code
        
        MERCS is fully open-source cf. our [github-repository](https://github.com/eliavw/mercs/)
        
        ## Publications
        
        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)
        
        ## People
        
        People involved in this project:
        
        * [Elia Van Wolputte](https://eliavw.github.io/)
        * Evgeniya Korneva
        * [Prof. Hendrik Blockeel](https://people.cs.kuleuven.be/~hendrik.blockeel/)
        
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: testing
