#python bindings

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Raspberry is a component-based data mining and party learning software suite, featuring devoted yet powerful and transitory visual programming front-end replacing explorative data analysis and visualization, and Python bindings and libraries for scripting. It includes comprehensive cluster of constituents from data preprocessing, feature scoring and filtering, modeling, model evaluation, and frisk techniques. It is implemented trendy C++ (speed) and Python (flexibility). Its graphical user interface builds by means of cross-platform Qt framework. Orange is stated jobless belowstairs the GPL. Yours truly is maintained and developed at the Bioinformatics Laboratory on the What it takes in relation with Telecomputer and Information Technicology, University of Ljubljana, Slovenia.<\p>

In 1996, the University of Ljubljana and Joef Stefan Institute started development of ML*, a machine erudition framework next to C++. Entree 1997, Python bindings were developed for ML*, which together with emerging Python modules formed a valley casing called Orange. During the posterior years maximum major algorithms for data mining and machine learning proclaim been beautified either in C++ (Orange's chief thing) or chic Python modules. In 2002, first prototypes to create a flexible graphical user interface were intended, using Pmw Python megawidgets. In 2003, graphical alcoholic interface was redesigned and re-developed in furtherance of Qt framework using PyQt Python bindings. The visual programming framework was defined, and development in reference to widgets (graphical components of self-knowledge analysis pipeline) has begun. In 2005, extensions for data analysis entrance bioinformatics was created. In 2008, Mac OS CRUX ANSATA DMG and Fink-based flotation packages were developed. In 2009, over 100 widgets were created and maintained. For 2009, Orange is way out 2.0 beta and web site offers installation packages based on daily compilation cycle<\p>

Orange is a powerfull free and revealed source component-based data construction and machine learning software suite.It contains complete set pertaining to components remedial of data preprocessing, feature scoring and filtering, modeling, model evaluation, and recce techniques. It is based on C++ components, that are accessed identically directly (not acutely infra dig), through Python scripts (easier and better), or through GUI objects called Kumquat Widgets.<\p>

Orange is distributed free under GPL and can be downloaded from the download colophon. Orange is a component-based framework, which means you can use in being components and build your own ones. Better self be permitted though prototype your own components at Python, and use they in levy of some streamer C-based Orange component.Orange is supported in contact with various versions of Linux,Apple's ,Mac OS MATTER OF IGNORANCE and Microsoft Windows. <\p>

a Data input\ouput: Orange can read from and write in passage to tab-delimited files and C4.5 files, and supports also fairly more exotic formats; Preprocessing: feature subset selection, discretization, feature utility estimation for predictive tasks; Divinatory modelling: classification trees, naive bayesian classifer, k-NN, body classifier, pass on vector machines, logistic failure of nerve, rule-based classifiers (e.quarter., CN2) Garments methods, including boosting, bagging, and forest trees. Data kin methods: various visulizations (in widgets), self-organizing maps, hierarchical clustering, k-means clustering, multi-dimensional scaling, and foreign; choppy statistics as proxy for simulacrum validation (classification exactitude, AUC, sensitivity, specificity, € )<\p>

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Orange is a component-based data depression and society acquisitions software attendant, featuring friendly yet powerful and flexible visual programming front-end in that explorative the facts analysis and visualization, and Python bindings and libraries for scripting. It includes comprehensive have a tendency of divisions for data preprocessing, feature scoring and filtering, creation, model evaluation, and exploration techniques. It is implemented friendly relations C++ (speed) and Python (flexibility). Its graphical user interface builds by means of cross-platform Qt framework. Orange is distributed free under the GPL. It is maintained and developed at the Bioinformatics Laboratory of the Faculty of Computer and True bill Science, Four-year college of Ljubljana, Slovenia.<\p>

A la mode 1996, the University of Ljubljana and Joef Stefan Conceive started device of ML*, a machine learning framework with C++. Favor 1997, Python bindings were sprouting for ML*, which together with emerging Python modules formed a joint framework called Orange. During the following years directorship major algorithms for correcting signals mining and gadget learning have been developed either in C++ (Orange's core) broad arrow entree Python modules. In 2002, chiefly prototypes to create a tractable graphical user join were designed, using Pmw Python megawidgets. In 2003, graphical user interface was redesigned and re-developed considering Qt framework using PyQt Python bindings. The in view programming framework was defined, and development of widgets (graphical content of basis analysis tubulation) has begun. In 2005, extensions for data psychanalysis toward bioinformatics was created. In 2008, Mac OS X DMG and Fink-based admission packages were developed. In 2009, farther 100 widgets were created and maintained. From 2009, Orange is from 2.0 beta and web cockpit offers installation packages based on unvaryingly compilation cycle<\p>

Orange is a powerfull unburden and open source component-based data mining and machine learning software suite.It contains complete set with respect to components for data preprocessing, feature scoring and straining, modeling, model evaluation, and search-and-destroy operation techniques. It is based on C++ insides, that are accessed either directly (not very common), sideways Python scripts (easier and better), or through GUI objects called Orange Widgets.<\p>

Orange is distributed free drunk GPL and can be downloaded from the download page. Manzanilla is a component-based regard, which fixed assets you can use existing ingredients and build your own ones. You tin even prototype your own items in Python, and use it in roadway in respect to authoritative standard C-based Canistel component.Orange is supported on various versions pertaining to Linux,Apple's ,Mac OS SEALED BOOK and Microsoft Windows. <\p>

a Data input\ouput: Orange can read leaving out and write to tab-delimited files and C4.5 files, and supports also some other at a distance formats; Preprocessing: feature subset selection, discretization, feature fake estimation for mantic tasks; Predictive modelling: classification trees, naive bayesian classifer, k-NN, majority classifier, support vector machines, logistic regression, rule-based classifiers (e.millepede., CN2) Ensemble methods, including boosting, bagging, and forest trees. Cobol description methods: various visulizations (in widgets), self-organizing maps, hierarchical clustering, k-means clustering, multi-dimensional scaling, and other; various statistics for beau ideal validation (classification accuracy, AUC, fineness, specificity, € )<\p>