Orange 2.6

Thu, 14 Feb 2013 18:15:08 -0000

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.

mloss.org Orange

Orange 2.6