source: orange/Orange/doc/ofb/classification.htm @ 9671:a7b056375472

Revision 9671:a7b056375472, 1.7 KB checked in by anze <anze.staric@…>, 2 years ago (diff)

Moved orange to Orange (part 2)

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1<html><HEAD>
2<LINK REL=StyleSheet HREF="../style.css" TYPE="text/css">
3</HEAD>
4<body>
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6<p class="Path">
7Prev: <a href="basic_exploration.htm">Basic Data Exploration</a>,
8Next: <a href="c_basics.htm">My First Orange Classifier</a>,
9Up: <a href="default.htm">On Tutorial 'Orange for Beginners'</a>
10</p>
11
12<H1>Classification</H1>
13<index name="classification">
14
15<p>A substantial part of Orange is devoted to machine learning
16methods for classification. These methods start from the data that
17incorporates classified instances (e.g., a collection of attribute
18values that are classified to a certain class), and attempt to
19develop models that would, given the set of attribute-values,
20predict a class for such instance.</p>
21
22<p>This part of tutorial will show you how to use several of
23Orange's classification methods. We start with a simple ones
24(<a href="c_basics.htm">naive Bayes</a>) and then demonstrate the
25use of <a href="c_otherclass.htm">several other</a> as well.
26Another issue we cover is <a href="c_performance.htm">assessing the
27predictive quality</a> of resulting classifiers. Orange was meant
28as a versatile tool that would also allow researchers to develop
29their own methods (or use a combination of existing methods). It is
30here where we think that Orange in combination with Python
31scripting really shines. Because Python may prove as a powerful
32tool to prototype and test your ideas, we include a special lesson
33on <a href="c_pythonlearner.htm">building the classifiers in
34Python</a>.</p>
35
36<hr><br><p class="Path">
37Prev: <a href="basic_exploration.htm">Basic Data Exploration</a>,
38Next: <a href="c_basics.htm">My First Orange Classifier</a>,
39Up: <a href="default.htm">On Tutorial 'Orange for Beginners'</a>
40</p>
41
42</body></html>
43
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