pycobra regression analysis and ensemble toolkithttp://mloss.orgUpdates and additions to pycobra regression analysis and ensemble toolkitenFri, 29 Dec 2017 13:57:46 -0000pycobra regression analysis and ensemble toolkit 0.2.2<html><p>pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem. </p> <p>pycobra offers a python implementation of the COBRA algorithm introduced by Biau et al. (2016) for regression. </p> <p>Another algorithm implemented is the EWA (Exponentially Weighted Aggregate) aggregation technique (among several other references, you can check the paper by Dalalyan and Tsybakov (2007). </p> <p>Apart from these two regression aggregation algorithms, pycobra implements a version of COBRA for classification. This procedure has been introduced by Mojirsheibani (1999). </p> <p>pycobra also offers various visualisation and diagnostic methods built on top of matplotlib which lets the user analyse and compare different regression machines with COBRA. The Visualisation class also lets you use some of the tools (such as Voronoi Tesselations) on other visualisation problems, such as clustering. </p> <p>References: </p> <ul> <li> G. Biau, A. Fischer, B. Guedj and J. D. Malley (2016), COBRA: A combined regression strategy, Journal of Multivariate Analysis. </li> <li> M. Mojirsheibani (1999), Combining Classifiers via Discretization, Journal of the American Statistical Association. </li> <li> A. S. Dalalyan and A. B. Tsybakov (2007) Aggregation by exponential weighting and sharp oracle inequalities, Conference on Learning Theory. </li> </ul></html>Bhargav Srinivasa Desikan, Benjamin GuedjFri, 29 Dec 2017 13:57:46 -0000 learningensemble methodsensemble learning