Description:

Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, like more complicated experimental setups or research of new machine learning methods, it provides an interface to Python, so it can be used a Python library. The system is designed to be easily extensible either in Python or in C++.

Orange includes

• preprocessing, for instance attribute ranking and selection, discretization, sampling, filtering);

• classification and regression methods, like classification trees, naive bayesian classifer, k-NN, majority classifier, support vector machines, logistic regression regression, rule-based classifiers (e.g., CN2), ensemble methods (boosting, bagging and random forests);

• procedurs for their validation, such as various sampling techniques and classification and regression scores;

• unsupervised methods, like association rules, self-organizing maps, hierarchical clustering, k-means clustering, multi-dimensional scaling;

• numerous simple and advanced visualizations - histograms and scatter plots, linear projections, parallel coordinates, radviz, sieve and mosaic diagrams.

Binaries are available for MS Windows and Mac. The code repository is hosted at https://bitbucket.org/biolab/orange.

Changes to previous version:

Added multi-target and multi-label learning, neural networks, Earth (MARS), PLS, and a faster tree induces for use in random forests; reorganization of module hierarchy; (weakly supported) Qwt has been replaced with a homemade module; networkx is used instead of a (weak) homemade structures for graphs; documentation has been moved to .rst, with a lot of it written anew or heavily redacted; improved system for registration of add-ons.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

URL: Project Homepage

Supported Operating Systems: Linux, Windows, Mac Os X

Data Formats: Various

Tags: Association Rules, Attribute Selection, Classification, Clustering, Preprocessing, Regression, Visualization

Archive: download here

Other available revisons

Version	Changelog	Date
2.6	Added multi-target and multi-label learning, neural networks, Earth (MARS), PLS, and a faster tree induces for use in random forests; reorganization of module hierarchy; (weakly supported) Qwt has been replaced with a homemade module; networkx is used instead of a (weak) homemade structures for graphs; documentation has been moved to .rst, with a lot of it written anew or heavily redacted; improved system for registration of add-ons.	February 14, 2013, 18:15:08
2.0 beta	Update for v2.0	August 23, 2010, 09:57:35
0.9.66	Initial Announcement on mloss.org.	November 14, 2007, 16:57:53

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