source: orange/docs/widgets/rst/regression/regressiontree.rst @ 11404:1a7b773d7c7b

Revision 11404:1a7b773d7c7b, 2.0 KB checked in by Ales Erjavec <ales.erjavec@…>, 14 months ago (diff)

Replaced the use of :code: role with :obj:

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1.. _Regression Tree:
2
3Regression Tree Learner
4=======================
5
6.. image:: ../icons/RegressionTree.png
7
8Regression Tree Learner
9
10Signals
11-------
12
13Inputs:
14   - Examples (ExampleTable)
15      A table with training examples
16
17
18Outputs:
19   - Learner
20      The classification tree learning algorithm with settings as specified in
21      the dialog.
22   - Regression Tree
23      Trained classifier (a subtype of Classifier)
24
25
26Signal :obj:`Regression Tree` sends data only if the learning data (signal
27:obj:`Examples`) is present.
28
29Description
30-----------
31
32This widget constructs a regression tree learning algorithm. As all widgets
33for classification and regression, this widget provides a learner and
34classifier/regressor on the output. Learner is a learning algorithm with
35settings as specified by the user. It can be fed into widgets for testing
36learners, for instance :ref:`Test Learners`.
37
38.. image:: images/RegressionTree.png
39   :alt: Regression Tree Widget
40
41Learner can be given a name under which it will appear in, say,
42:ref:`Test Learners`. The default name is "Regression Tree".
43
44If :obj:`Binarization` is checked, the values of multivalued attributes
45are split into two groups (based on the statistics in the particular node)
46to yield a binary tree. Binarization gets rid of the usual measures' bias
47towards attributes with more values and is generally recommended.
48
49The widget can be instructed to prune the tree during induction by setting
50:obj:`Do not split nodes with less instances than`. For pruning after
51induction, there is pruning with m-estimate of error.
52
53After changing one or more settings, you need to push :obj:`Apply`, which will
54put the new learner on the output and, if the training examples are given,
55construct a new classifier and output it as well.
56
57Examples
58--------
59
60There are two typical uses of this widget. First, you may want to induce
61the model and check what it looks like with the schema below.
62
63.. image:: images/RegressionTree-Schema.png
64
65The second schema checks the accuracy of the algorithm.
66
67.. image:: images/RegressionTree-Schema2.png
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