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7<h1>Orange Modules</h1>
9<p>Orange modules are intended to extend basic Orange's functionality, or provide wrappers for easier use of some data mining techniques. They are already included in normal Orange distribution. Following set of pages provides description, demos and examples for selected modules.</p>
11<P>Note: The documentation posted on the web is updated from the CVS in
12real-time and refers to the latest snapshot of Orange. If you encounter
13any inconsistencies please compare the standalone documentation with the
14one on the web.</P>
17<dt><a href="orngAssoc.htm">orngAssoc</a></dt><dd>A few things for association rules.</dd>
18<dt><a href="orngBayes.htm">orngBayes</a></dt><dd>Wrapper around Orange's naive Bayesian learner that makes it easier to use m-estimation; it can also print out the model.</dd>
19<dt><a href="orngCA.htm">orngCA</a></dt><dd>Correspondence analysis.</dd>
20<dt><a href="orngC45.htm">orngC45</a></dt><dd>A module with a function that prints out C4.5 trees in exactly the same format as Quinlan's C4.5.</dd>
21<dt><a href="orngCI.htm">orngCI</a></dt><dd>Constructive induction (function decomposition methods, HINT, Kramer's constructive induction method).</dd>
22<dt><a href="orngClustering.htm">orngClustering</a></dt><dd>Various clustering methods and associated miscellaneous utilities.</dd>
23<dt><a href="orngCN2.htm">orngCN2</a></dt><dd>A set of classes and functions for learning rules (based on CN2).</dd>
24<dt><a href="orngDisc.htm">orngDisc</a></dt><dd>Wrapper around Orange's categorization techniques for continuous attributes.</dd>
25<dt><a href="orngEnsemble.htm">orngEnsemble</a></dt><dd>Bagging,
26    boosting, random forests.</dd>
27<dt>orngEval</dt><dd>Obsolete, included for compatibility with past version of Orange. Use orngTest and orngStat instead.</dd>
28<dt><a href="orngFSS.htm">orngFSS</a></dt><dd>Feature subset selection.</dd>
29<dt><a href="orngImpute.htm">orngImpute</a></dt><dd>Imputation wrappers for learners and classifiers.</dd>
30<dt><a href="orngLinProj.htm">orngLinProj</a></dt><dd>Implements the FreeViz method by Demsar et al.</dd>
31<dt><a href="orngLookup.htm">orngLookup</a></dt><dd>Functions for working with classifiers with stored tables of examples.</dd>
32<dt><a href="orngLR.htm">orngLR</a></dt><dd>Wrappers for easier use of Orange's classes for logistic regression</dd>
33<dt><a href="orngMDS.htm">orngMDS</a></dt><dd>Multidimensional scaling.</dd>
34<dt><a href="orngMisc.htm">orngMisc</a></dt><dd>Miscellaneous functions, including various counters and selections of optimal objects in a sequence.</dd>
35<dt><a href="orngMySQL.htm">orngMySQL</a></dt><dd>Interface to MySQL.</dd>
36<dt><a href="orngNetwork.htm">orngNetwork</a></dt><dd>Network analysis and layout optimization.</dd>
37<dt><a href="orngOutlier.htm">orngOutlier</a></dt><dd>Outlier detection.</dd>
38<dt><a href="orngReinforcement.htm">orngReinforcement</a></dt><dd>Reinforcement
40<dt><a href="orngServerFiles.htm">orngServerFiles</a></dt><dd>Orange's file repository.</dd>
41<dt><a href="orngSQL.htm">orngSQL</a></dt><dd>A new interface to any <a href="">PEP 249</a> compliant RDBS. Supports both MySQL and Postgres.</dd>
42<dt><a href="orngStat.htm">orngStat</a></dt><dd>Computation of various statistics such as accuracy, sensitivity, specificity, and area under ROC from the experimental data from module orngTest.</dd>
43<dt><a href="orngSVM.htm">orngSVM</a></dt><dd>Support vector machines.</dd>
44<dt><a href="orngTest.htm">orngTest</a></dt><dd>Data sampling and testing of learners, for instance cross-validation, leave-one out, random sampling, etc. The results can be used by orngStat to compute various statistics.</dd>
45<dt><a href="orngTree.htm">orngTree</a></dt><dd>Wrapper around Orange's classification tree inducer. Most notably, implements tree printout and visualization.</dd>
46<dt><a href="orngVizRank.htm">orngVizRank</a></dt><dd>Implementation
47    of VizRank intelligent visualization method.</dd>
48<dt><a href="orngWrap.htm">orngWrap</a></dt><dd>Classes for tuning arguments using internal cross-validation and for searching for threshold for optimal classification accuracy.</dd>
51<p>Additional modules for data mining (clustering, SVM wrapper for
52probability estimation, logistic regression, density estimation...)
53have been prepared by Aleks Jakulin and are accessible via <a href=
54"">his pages</a>.</p>
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