We introduce \texttt{pycobra}, a Python library devoted to ensemble learning
(regression and classification) and visualisation. Its main assets are the
implementation of several ensemble learning algorithms, a flexible and generic
interface to compare and blend any existing machine learning algorithm
available in Python libraries (as long as a \texttt{predict} method is given),
and visualisation tools such as Voronoi tessellations. \texttt{pycobra} is
fully \texttt{scikit-learn} compatible and is released under the MIT
open-source license. \texttt{pycobra} can be downloaded from the Python Package
Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current
version (along with Jupyter notebooks, extensive documentation, and continuous
integration tests) is available at
\href{https://github.com/bhargavvader/pycobra}{https://github.com/bhargavvader/pycobra}
and official documentation website is
\href{https://modal.lille.inria.fr/pycobra}{https://modal.lille.inria.fr/pycobra}

Desikan, Bhargav Srinivasa

Guedj, Benjamin

English

arXiv

International audienceWe introduce pycobra, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a predict method is given), and visualisation tools such as Voronoi tessellations. pycobra is fully scikit-learn compatible and is released under the MIT open-source license. pycobra can be down-loaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at https://github.com/bhargavvader/pycobra

Srinivasa Desikan, Bhargav

INRIA a CCSD electronic archive server

HAL Id: hal-01514059https://hal.inria.fr/hal-01514059v3Submitted on 20 Jun 2018HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Distributed under a Creative Commons Attribution - NonCommercial - ShareAlike| 4.0International LicensePycobra: A Python Toolbox for Ensemble Learning andVisualisationBenjamin Guedj, Bhargav Srinivasa DesikanTo cite this version:Benjamin Guedj, Bhargav Srinivasa Desikan. Pycobra: A Python Toolbox for Ensemble Learningand Visualisation. Journal of Machine Learning Research, Microtome Publishing, 2018, 18, pp.1 - 5.￿hal-01514059v3￿Journal of Machine Learning Research 18 (2018) 1-5 Submitted 4/17; Revised 3/18; Published 4/18Pycobra: A Python Toolbox for Ensemble Learning andVisualisationBenjamin Guedj benjamin.guedj@inria.frModal project-team, Lille - Nord Europe research centerInria, FranceBhargav Srinivasa Desikan bhargav.srinivasa-desikan@inria.frModal project-team, Lille - Nord Europe research centerInria, FranceEditor: Geoff HolmesAbstractWe introduce pycobra, a Python library devoted to ensemble learning (regression and clas-sification) and visualisation. Its main assets are the implementation of several ensemblelearning algorithms, a flexible and generic interface to compare and blend any existingmachine learning algorithm available in Python libraries (as long as a predict method isgiven), and visualisation tools such as Voronoi tessellations. pycobra is fully scikit-learncompatible and is released under the MIT open-source license. pycobra can be down-loaded from the Python Package Index (PyPi) and Machine Learning Open Source Software(MLOSS). The current version (along with Jupyter notebooks, extensive documentation,and continuous integration tests) is available at https://github.com/bhargavvader/pycobraand official documentation website is https://modal.lille.inria.fr/pycobra.Keywords: ensemble methods, machine learning, Voronoi tesselation, Python, opensource software1. IntroductionCombined statistical procedures, also known as ensemble or aggregation methods, are verypopular in Machine Learning competitions – the celebrated Netflix Challenge (as are re-peatedly Kaggle competitions) was won by an ensemble technique (see for example Bell andKoren, 2007, for a discussion). In a nutshell, ensemble methods combine different predictors(each trained on the same dataset) to improve performance. scikit-learn offers imple-mentations of some ensemble methods but it does not ship with a specific analysis toolboxfor aggregation. pycobra attempts to fill this gap by providing to the scikit-learn envi-ronment a toolbox to analyse and visualise the different preliminary predictors (also calledmachines hereafter), in addition to providing pythonic implementations of several ensem-ble algorithms (the COBRA algorithm introduced by Biau et al., 2016; the ExponentialWeighted Aggregate (EWA) introduced by Vovk, 1990; a COBRA-flavored majority voteinspired by Mojirsheibani, 1999). All these algorithms are supported by oracle boundswhich prove that the risk of the aggregated predictor is upper-bounded by the smallest riskof the initial pool of predictors, up to a remainder term which decays to zero (typically ata rate O(1/√n) or faster).c©2018 Benjamin Guedj and Bhargav Srinivasa Desikan.License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are providedat http://jmlr.org/papers/v18/17-228.html.Guedj and Srinivasa DesikanCOBRA (standing for COmBined Regression Alternative) is a nonlinear ensemble methoddesigned for regression problems. To predict the response for a new data point, COBRAoperates in two steps. First, it retains data points for which the prediction made by pre-liminary machines is close (in the Euclidean sense) to the prediction made for the newpoint. This is called the consensus step. Second, the final prediction is then formed byaveraging responses over the retained points’ indices. COBRA outputs a predictor whichoutperforms any combination of the preliminary predictors (as shown by see Theorem 2.1in Biau et al., 2016). We describe the COBRA algorithm as implemented in pycobra inAlgorithm 1. Exponential weights have been used for a long time in statistical learningtheory (Vovk, 1990). The EWA algorithm implemented in Pycobra is inspired by the de-scription of Dalalyan and Tsybakov (2007). EWA amounts to forming an exponentiallyweighted average of preliminary predictors, where the weights include a measure of eachpredictor’s performance. COBRA has been inspired by the work of Mojirsheibani (1999)in classification, and therefore pycobra includes a classification version of COBRA calledClassifierCobra. The consensus step shares a similar philosophy to what is done in CO-BRA regression but the final prediction is delivered by a majority vote among the labels ofthe retained data points.pycobra allows the user to gauge the performance of the preliminary predictors used in theaggregation, with built-in methods to easily plot boxplots and QQ-plots. A salient feature ofpycobra is using Voronoi tessellations for generic visualisation. By implementing ensemblealgorithms which were not represented in the Python machine learning community, andproviding a variety of tools to visualise and analyse their behavior and performance, wepresent to the machine learning open source community a toolbox designed for ensemblelearning and visualisation.2. The pycobra libraryOur toolbox is written in Python and uses NumPy (Walt et al., 2011) and scikit-learn (Pe-dregosa et al., 2011) for computation and linear algebra operations. Matplotlib (Hunter,2007) is used for visualisation purposes, and SciPy (Jones et al., 2001) is used to helpcreate Voronoi tessellations. Tutorials and examples are created using Jupyter IPythonnotebooks (Pérez and Granger, 2007). Currently pycobra supports any machine learningalgorithm with a predict method, casting our procedure into a very flexible and genericframework. By default, pycobra relies on scikit-learn implementations of Lasso, RandomForest, Decision Trees and Ridge regression for the EWA and COBRA implementations. As forClassifierCobra, scikit-learn implementations of SVM classifier, KNN, Decision Treeclassifier, and a classifier which implements regularised linear models with SGD (SGDClas-sifier object). pycobra itself and all software it relies on is open source, with no dependenceon proprietary software. Algorithm 1 presents the pseudo-code of the COBRA implemen-tation. All the pycobra estimators are scikit-learn compatible and can be used as partof the existing scikit-learn ecosystem, such as [GridSearchCV] and [Pipeline]. Whilehyperparameter initialisation is systematically done using scikit-learn’s GridSearchCV,pycobra’s Diagnostics class allows us to compare between different combinations of theconstituent predictors and data-splitting, among other basic parameters.2PycobraAlgorithm 1: The original COBRA algorithm from Biau et al. (2016).Data: input vector X, epsilon, alpha, basic-machines, training-set-responses, training-set# training-set is the set composed of all data point and the responses. training-set-responses is the setcomposed of the responses.Result: prediction Yfor machine j in basic-machines domachine set = ;# machine set is a dictionary mapping each machine M to a set machine set[M ] ;pred = rj(X); # where rj(x) denotes the prediction made by machine j at point x;for response in training-set-responses doif |data point− pred| ≤ epsilon, collect response in machine set[M ] ;endendarray = [ ] ;for data point in training-set doif at least alpha machines out of M have collected data point in their set, store point in array ;endresult = average(array) ;The visualisation class allows the user to compare all the machines used to create ag-gregates, as well as visualise the results, for all pycobra estimators. pycobra ships with [anotebook on visualisation] to illustrate this.QQ-plots and boxplots. Once all the basic machines are initialized and trained, theuser can easily compare their performance with boxplots and QQ-plots. All the plottingdetails are handled by both the diagnostics and visualisation classes. An example isgiven in Figure 1.Visualisation through Voronoi tessellations. Voronoi tessellations can be used tovisualise the selection of optimal machines for COBRA, as well as visualising outputs fromclustering algorithms, as illustrated by the [tutorial notebook]. An example is reproducedFigure 2.3. Project FocusCommunity-based development. We intend pycobra to be an ongoing collaborativeproject. To that matter, it is referenced on [Python Package Index (PyPi)] and [MachineLearning Open Source Software]. Moreover, pycobra is under active development and avail-Figure 1: Assessing the performance of regression machines and COBRA.3Guedj and Srinivasa DesikanFigure 2: Visualising clustering through Voronoi tessellations.able on [GitHub] to promote collaborative programming, issue tracking and idea discussions.Documentation and Jupyter Notebooks. A consistent API documentation is pro-vided, along with an additional installation guide and examples. The documentation web-site is available at https://modal.lille.inria.fr/pycobra. The [notebooks directory] containsJupyter notebooks which serve as both a documentation tool and a tutorial resource. Thesenotebooks cover use-cases of Pycobra, from solving regression and classification problemsto using Voronoi tesselations.Ease of use and quality assurance. Ensemble learning with pycobra is as simple asloading trained scikit-learn machines – or any machine which has a predict method.Visualising involves little to no parameters, and after loading machines it is straightforwardto analyse their performance on a particular dataset. In order to ensure code quality, a setof unit tests is provided for all classes in pycobra, and continuous integration via [TravisCI] ensures all commits are tested. The package follows the PEP8 convention, keeping thecode easy to read and contribute to.4. Conclusion and Future WorkThe future of pycobra would be to grow its user base by adding new ways to add predictors,as well as further implement ensemble learning techniques and visualisation tools. Statisticalaggregation and ensemble learning are an important part of the machine learning literatureand are widely used by practitioners, yet it seems under-represented in the machine learningopen source community. By creating pycobra and releasing it to the community, we intendto enrich the existing ecosystem with ensemble algorithms, a generic toolbox for ensemblelearning on-the-go, along with analysis and visualisation tools.4PycobraReferencesRobert M. Bell and Yehuda Koren. Lessons from the Netflix prize challenge. ACM SIGKDDExplorations Newsletter, 9(2):75–79, 2007.Gérard Biau, Aurélie Fischer, Benjamin Guedj, and James D. Malley. COBRA: A combinedregression strategy. Journal of Multivariate Analysis, 146:18–28, 2016.Arnak Dalalyan and Alexandre Tsybakov. Aggregation by exponential weighting and sharporacle inequalities. In Computational Learning theory (COLT), pages 97–111. Springer,2007.John D. Hunter. Matplotlib: A 2D graphics environment. Computing in Science & Engi-neering, 9(3):90–95, 2007.Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools forPython, 2001. URL http://www.scipy.org/.Majid Mojirsheibani. Combining classifiers via discretization. Journal of the AmericanStatistical Association, 94(446):600–609, 1999.Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, BertrandThirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, VincentDubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher,Matthieu Perrot, and Édouard Duchesnay. Scikit-learn: Machine learning in python.Journal of Machine Learning Research, 12(Oct):2825–2830, 2011.Fernando Pérez and Brian E. Granger. IPython: a system for interactive scientific comput-ing. Computing in Science & Engineering, 9(3):21–29, May 2007. ISSN 1521-9615. doi:10.1109/MCSE.2007.53. URL http://ipython.org.Volodimir G. Vovk. Aggregating strategies. In Computational Learning theory (COLT),pages 371–386. Morgan Kaufmann Publishers Inc., 1990.Stéfan van der Walt, S. Chris Colbert, and Gaël Varoquaux. The NumPy array: a structurefor efficient numerical computation. Computing in Science & Engineering, 13(2):22–30,2011.5

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

We introduce pycobra, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a predict method is given), and visualisation tools such as Voronoi tessellations. pycobra is fully scikit-learn compatible and is released under the MIT open-source license. pycobra can be downloaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at https://github.com/bhargavvader/pycobra and official documentation website is https://modal.Lille.inria.fr/pycobra

Guedj, B

Desikan, BS

UCL Discovery

Journal of Machine Learning Research 18 (2018) 1-5 Submitted 4/17; Revised 3/18; Published 4/18Pycobra: A Python Toolbox for Ensemble Learning andVisualisationBenjamin Guedj benjamin.guedj@inria.frModal project-team, Lille - Nord Europe research centerInria, FranceBhargav Srinivasa Desikan bhargav.srinivasa-desikan@inria.frModal project-team, Lille - Nord Europe research centerInria, FranceEditor: Geoff HolmesAbstractWe introduce pycobra, a Python library devoted to ensemble learning (regression and clas-sification) and visualisation. Its main assets are the implementation of several ensemblelearning algorithms, a flexible and generic interface to compare and blend any existingmachine learning algorithm available in Python libraries (as long as a predict method isgiven), and visualisation tools such as Voronoi tessellations. pycobra is fully scikit-learncompatible and is released under the MIT open-source license. pycobra can be down-loaded from the Python Package Index (PyPi) and Machine Learning Open Source Software(MLOSS). The current version (along with Jupyter notebooks, extensive documentation,and continuous integration tests) is available at https://github.com/bhargavvader/pycobraand official documentation website is https://modal.lille.inria.fr/pycobra.Keywords: ensemble methods, machine learning, Voronoi tesselation, Python, opensource software1. IntroductionCombined statistical procedures, also known as ensemble or aggregation methods, are verypopular in Machine Learning competitions – the celebrated Netflix Challenge (as are re-peatedly Kaggle competitions) was won by an ensemble technique (see for example Bell andKoren, 2007, for a discussion). In a nutshell, ensemble methods combine different predictors(each trained on the same dataset) to improve performance. scikit-learn offers imple-mentations of some ensemble methods but it does not ship with a specific analysis toolboxfor aggregation. pycobra attempts to fill this gap by providing to the scikit-learn envi-ronment a toolbox to analyse and visualise the different preliminary predictors (also calledmachines hereafter), in addition to providing pythonic implementations of several ensem-ble algorithms (the COBRA algorithm introduced by Biau et al., 2016; the ExponentialWeighted Aggregate (EWA) introduced by Vovk, 1990; a COBRA-flavored majority voteinspired by Mojirsheibani, 1999). All these algorithms are supported by oracle boundswhich prove that the risk of the aggregated predictor is upper-bounded by the smallest riskof the initial pool of predictors, up to a remainder term which decays to zero (typically ata rate O(1/√n) or faster).c©2018 Benjamin Guedj and Bhargav Srinivasa Desikan.License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are providedat http://jmlr.org/papers/v18/17-228.html.Guedj and Srinivasa DesikanCOBRA (standing for COmBined Regression Alternative) is a nonlinear ensemble methoddesigned for regression problems. To predict the response for a new data point, COBRAoperates in two steps. First, it retains data points for which the prediction made by pre-liminary machines is close (in the Euclidean sense) to the prediction made for the newpoint. This is called the consensus step. Second, the final prediction is then formed byaveraging responses over the retained points’ indices. COBRA outputs a predictor whichoutperforms any combination of the preliminary predictors (as shown by see Theorem 2.1in Biau et al., 2016). We describe the COBRA algorithm as implemented in pycobra inAlgorithm 1. Exponential weights have been used for a long time in statistical learningtheory (Vovk, 1990). The EWA algorithm implemented in Pycobra is inspired by the de-scription of Dalalyan and Tsybakov (2007). EWA amounts to forming an exponentiallyweighted average of preliminary predictors, where the weights include a measure of eachpredictor’s performance. COBRA has been inspired by the work of Mojirsheibani (1999)in classification, and therefore pycobra includes a classification version of COBRA calledClassifierCobra. The consensus step shares a similar philosophy to what is done in CO-BRA regression but the final prediction is delivered by a majority vote among the labels ofthe retained data points.pycobra allows the user to gauge the performance of the preliminary predictors used in theaggregation, with built-in methods to easily plot boxplots and QQ-plots. A salient feature ofpycobra is using Voronoi tessellations for generic visualisation. By implementing ensemblealgorithms which were not represented in the Python machine learning community, andproviding a variety of tools to visualise and analyse their behavior and performance, wepresent to the machine learning open source community a toolbox designed for ensemblelearning and visualisation.2. The pycobra libraryOur toolbox is written in Python and uses NumPy (Walt et al., 2011) and scikit-learn (Pe-dregosa et al., 2011) for computation and linear algebra operations. Matplotlib (Hunter,2007) is used for visualisation purposes, and SciPy (Jones et al., 2001) is used to helpcreate Voronoi tessellations. Tutorials and examples are created using Jupyter IPythonnotebooks (Pe´rez and Granger, 2007). Currently pycobra supports any machine learningalgorithm with a predict method, casting our procedure into a very flexible and genericframework. By default, pycobra relies on scikit-learn implementations of Lasso, RandomForest, Decision Trees and Ridge regression for the EWA and COBRA implementations. As forClassifierCobra, scikit-learn implementations of SVM classifier, KNN, Decision Treeclassifier, and a classifier which implements regularised linear models with SGD (SGDClas-sifier object). pycobra itself and all software it relies on is open source, with no dependenceon proprietary software. Algorithm 1 presents the pseudo-code of the COBRA implemen-tation. All the pycobra estimators are scikit-learn compatible and can be used as partof the existing scikit-learn ecosystem, such as [GridSearchCV] and [Pipeline]. Whilehyperparameter initialisation is systematically done using scikit-learn’s GridSearchCV,pycobra’s Diagnostics class allows us to compare between different combinations of theconstituent predictors and data-splitting, among other basic parameters.2PycobraAlgorithm 1: The original COBRA algorithm from Biau et al. (2016).Data: input vector X, epsilon, alpha, basic-machines, training-set-responses, training-set# training-set is the set composed of all data point and the responses. training-set-responses is the setcomposed of the responses.Result: prediction Yfor machine j in basic-machines domachine set = ;# machine set is a dictionary mapping each machine M to a set machine set[M ] ;pred = rj(X); # where rj(x) denotes the prediction made by machine j at point x;for response in training-set-responses doif |data point− pred| ≤ epsilon, collect response in machine set[M ] ;endendarray = [ ] ;for data point in training-set doif at least alpha machines out of M have collected data point in their set, store point in array ;endresult = average(array) ;The visualisation class allows the user to compare all the machines used to create ag-gregates, as well as visualise the results, for all pycobra estimators. pycobra ships with [anotebook on visualisation] to illustrate this.QQ-plots and boxplots. Once all the basic machines are initialized and trained, theuser can easily compare their performance with boxplots and QQ-plots. All the plottingdetails are handled by both the diagnostics and visualisation classes. An example isgiven in Figure 1.Visualisation through Voronoi tessellations. Voronoi tessellations can be used tovisualise the selection of optimal machines for COBRA, as well as visualising outputs fromclustering algorithms, as illustrated by the [tutorial notebook]. An example is reproducedFigure 2.3. Project FocusCommunity-based development. We intend pycobra to be an ongoing collaborativeproject. To that matter, it is referenced on [Python Package Index (PyPi)] and [MachineLearning Open Source Software]. Moreover, pycobra is under active development and avail-Figure 1: Assessing the performance of regression machines and COBRA.3Guedj and Srinivasa DesikanFigure 2: Visualising clustering through Voronoi tessellations.able on [GitHub] to promote collaborative programming, issue tracking and idea discussions.Documentation and Jupyter Notebooks. A consistent API documentation is pro-vided, along with an additional installation guide and examples. The documentation web-site is available at https://modal.lille.inria.fr/pycobra. The [notebooks directory] containsJupyter notebooks which serve as both a documentation tool and a tutorial resource. Thesenotebooks cover use-cases of Pycobra, from solving regression and classification problemsto using Voronoi tesselations.Ease of use and quality assurance. Ensemble learning with pycobra is as simple asloading trained scikit-learn machines – or any machine which has a predict method.Visualising involves little to no parameters, and after loading machines it is straightforwardto analyse their performance on a particular dataset. In order to ensure code quality, a setof unit tests is provided for all classes in pycobra, and continuous integration via [TravisCI] ensures all commits are tested. The package follows the PEP8 convention, keeping thecode easy to read and contribute to.4. Conclusion and Future WorkThe future of pycobra would be to grow its user base by adding new ways to add predictors,as well as further implement ensemble learning techniques and visualisation tools. Statisticalaggregation and ensemble learning are an important part of the machine learning literatureand are widely used by practitioners, yet it seems under-represented in the machine learningopen source community. By creating pycobra and releasing it to the community, we intendto enrich the existing ecosystem with ensemble algorithms, a generic toolbox for ensemblelearning on-the-go, along with analysis and visualisation tools.4PycobraReferencesRobert M. Bell and Yehuda Koren. Lessons from the Netflix prize challenge. ACM SIGKDDExplorations Newsletter, 9(2):75–79, 2007.Ge´rard Biau, Aure´lie Fischer, Benjamin Guedj, and James D. Malley. COBRA: A combinedregression strategy. Journal of Multivariate Analysis, 146:18–28, 2016.Arnak Dalalyan and Alexandre Tsybakov. Aggregation by exponential weighting and sharporacle inequalities. In Computational Learning theory (COLT), pages 97–111. Springer,2007.John D. Hunter. Matplotlib: A 2D graphics environment. Computing in Science & Engi-neering, 9(3):90–95, 2007.Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools forPython, 2001. URL http://www.scipy.org/.Majid Mojirsheibani. Combining classifiers via discretization. Journal of the AmericanStatistical Association, 94(446):600–609, 1999.Fabian Pedregosa, Gae¨l Varoquaux, Alexandre Gramfort, Vincent Michel, BertrandThirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, VincentDubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher,Matthieu Perrot, and E´douard Duchesnay. Scikit-learn: Machine learning in python.Journal of Machine Learning Research, 12(Oct):2825–2830, 2011.Fernando Pe´rez and Brian E. Granger. IPython: a system for interactive scientific comput-ing. Computing in Science & Engineering, 9(3):21–29, May 2007. ISSN 1521-9615. doi:10.1109/MCSE.2007.53. URL http://ipython.org.Volodimir G. Vovk. Aggregating strategies. In Computational Learning theory (COLT),pages 371–386. Morgan Kaufmann Publishers Inc., 1990.Ste´fan van der Walt, S. Chris Colbert, and Gae¨l Varoquaux. The NumPy array: a structurefor efficient numerical computation. Computing in Science & Engineering, 13(2):22–30,2011.5

Pycobra: A python toolbox for ensemble learning and visualisation

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Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

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