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 also be called from Python scripts. 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 - supervised methods: classification trees, naive bayesian classifer, k-NN, majority classifier, support vector machines, logistic - regression, rule-based classifiers (e.g., CN2), ensemble methods (boosting, bagging and random forests) and techniques for their validation - unsupervised methods: 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
- Changes to previous version:
Update for v2.0
Other available revisons
Version Changelog Date 2.6
The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL.
Changed the BibTeX reference to the paper recently published in JMLR MLOSS.
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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