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9Up: <a href="..">Orange Documentation</a>
10</p>
11
12<H1>Orange for Beginners</H1>
13
14<p>If you are new to Orange, than this is probably the best place
15to start. &ldquo;Beginning with Orange&rdquo; was written with a
16purpose to provide a gentle tutorial over basic functionality of
17Orange. The tutorial includes:</p>
18
19<ul>
20
22and import Orange,</li>
23
26
27<li><A href="basic_exploration.htm">Basic data exploration</A>
28demonstrates how to perform some basic statistical analysis, </li>
29
30<li>
31  <A href="classification.htm">Classification</A> describes how to set-up the learners, build classifiers, and evaluate them using cross-validation or alike, </li>
32
33   <ul>
34
35   <li><A href="c_basics.htm">My first Orange classifier</A> builds
36   naive Bayesian classifier and shows how to use to predict class
37   labels and probabilities,</li>
38
39   <li><A href="c_otherclass.htm">Selected classification methods</A>
40   lists other classification methods provided by Orange,</li>
41
42   <li><A href="c_performance.htm">Testing and evaluating</A> speaks
43   about of testing and comparison of learners, </li>
44
45   <li><A href="c_pythonlearner.htm">Build your own learner</A> is a
46   chapter on how to prototype your own learners/classifiers in
47   Python.</li>
48
49</ul>
50
51<li><A href="regression.htm">Regression</A> introduces several methods
52that can be used for regression modelling, </li>
53
54<li><A href="assoc.htm">Association rules</A> shows how to use
55Orange's association rules,</li>
56
57<li><A href="other.htm">Other topics</A></li>
58
59   <ul>
60
61   <li><A href="o_categorization.htm">Categorization</A> lists several
62   methods within Orange that can be used for categorization, </li>
63
64   <li><A href="o_fss.htm">Feature subset selection</A></li>
65
66   <li><A href="o_ensemble.htm">Ensemble techniques</A> discusses how
67   to use a particular Orange's module for boosting and bagging, </li>
68
69   </ul>
70
71<li><A href="domain.htm">Basic Data Manipulation</A> illustrates how
72to select attributes, instances, craft new domains and data sets, and
73alike. Finishes with wrapper-based feature subset selector, </li>
74
75<li><A href="uncovered.htm">What we did not cover</A></li>
76
77</ul>
78
79<p>As Orange is integrated within Python, the tutorial is in essence a
80guide through some basic Orange scripting in this language. Although
81relying on Python, those of you who have some knowledge on programming
82won&rsquo;t need to learn Python first: the tutorial should be simple
83enough to skip learning Python itself. If, however, you will later
84want to develop your own methods and test some of your more complex
86href="http://www.python.org/">Python&rsquo;s site</a> for some
87material on this rather new and appealing scripting language.</p>
88
89<p>Through the tutorial, some basic python scripts are developed that
90most often read the data and do something with it. You will find that
91every piece of code we present has a pointer to the file with this
92code that you can download (use the mouse, press right-button on the
93link, and choose &ldquo;Save Target As &hellip;&rdquo;. You will also
94find that with it are pointers to the data files that you can download
95too. We recommend you to first open a special folder where you want to
96save both the scripts and files that come with them.</p>
97
98<p>We expect this tutorial to be interactive, that is, we would like
101how to start Orange scripting</a>.</p>
102
105documentation</a> that also includes all script files and data
106files.</p>
107
108<P>Note: The documentation posted on the web is updated from the CVS in
109real-time and refers to the latest snapshot of Orange. If you encounter
110any inconsistencies please compare the standalone documentation with the
111one on the web.</P>
112