Available on ORBi since
05 February 2014
Poster (Scientific congresses and symposiums)
Scikit-Learn: Machine Learning in the Python ecosystem
Joly, Arnaud  ; Louppe, Gilles 
2014 • GIGA DAY 2014


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Keywords :
machine learning; open source software
Abstract :
[en] The scikit-learn project is an increasingly popular machine learning library written in Python. It is designed to be simple and efficient, useful to both experts and non-experts, and reusable in a variety of contexts. The primary aim of the project is to provide a compendium of efficient implementations of classic, well-established machine learning algorithms. Among other things, it includes classical supervised and unsupervised learning algorithms, tools for model evaluation and selection, as well as tools for data preprocessing and feature engineering. This presentation will illustrate the use of scikit-learn as a component of the larger scientific Python environment to solve complex data analysis tasks. Examples will include end-to-end workflows based on powerful and popular algorithms in the library. Among others, we will show how to use out-of-core learning with on-the-fly feature extraction to tackle very large natural language processing tasks, how to exploit an IPython cluster for distributed cross-validation, or how to build and use random forests to explore biological data.
Disciplines :
Computer science
Author, co-author :
Joly, Arnaud ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Louppe, Gilles  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
Title :
Scikit-Learn: Machine Learning in the Python ecosystem
Publication date :
27 January 2014
Event name :
Event place :
Liège, Belgium
Event date :
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique
Commentary :
See also http://orbi.ulg.ac.be/handle/2268/157487


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