source: orange/docs/widgets/rst/classify/logisticregression.rst @ 11404:1a7b773d7c7b

Revision 11404:1a7b773d7c7b, 2.5 KB checked in by Ales Erjavec <ales.erjavec@…>, 14 months ago (diff)

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

Line 
1.. _Logistic Regression:
2
3Logistic Regression Learner
4===========================
5
6.. image:: ../icons/LogisticRegression.png
7
8Logistic Regression Learner
9
10Signals
11-------
12
13Inputs:
14
15
16   - Examples (ExampleTable)
17      A table with training examples
18
19
20Outputs:
21
22   - Learner
23      The logistic regression learning algorithm with settings as specified
24      in the dialog.
25
26   - Logistic Regression Classifier
27      Trained classifier (a subtype of Classifier)
28
29
30Signal :obj:`Logistic Regression Classifier` sends data only if the learning
31data (signal :obj:`Examples` is present.
32
33Description
34-----------
35
36This widget provides a graphical interface to the logistic regression
37classifier.
38
39As all widgets for classification, this widget provides a learner and
40classifier on the output. Learner is a learning algorithm with settings
41as specified by the user. It can be fed into widgets for testing learners,
42for instance :ref:`Test Learners`. Classifier is a logistic regression
43classifier (a subtype of a general classifier), built from the training
44examples on the input. If examples are not given, there is no classifier
45on the output.
46
47The widget requires - due to limitations of the learning algorithm - data with
48binary class.
49
50.. image:: images/LogisticRegression.png
51   :alt: Logistic Regression Widget
52
53Learner can be given a name under which it will appear in, say,
54:ref:`Test Learners`. The default name is "Logistic Regression".
55
56If :obj:`Stepwise attribute selection` is checked, the learner will
57iteratively add and remove the attributes, one at a time, based on their
58significance. The thresholds for addition and removal of the attribute are
59set in :obj:`Add threshold` and :obj:`Remove threshold`. It is also possible
60to limit the total number of attributes in the model.
61
62Independent of these settings, the learner will always remove singular
63attributes, for instance the constant attributes or those which can be
64expressed as a linear combination of other attributes.
65
66Logistic regression has no internal mechanism for dealing with missing
67values. These thus need to be imputed. The widget offers a number of options:
68it can impute the average value of the attribute, its minimum and maximum or
69train a model to predict the attribute's values based on values of other
70attributes. It can also remove the examples with missing values.
71
72Note that there also exist a separate widget for missing data imputation,
73:ref:`Impute`.
74
75
76Examples
77--------
78
79The widget is used just as any other widget for inducing classifier. See,
80for instance, the example for the :ref:`Naive Bayes`.
Note: See TracBrowser for help on using the repository browser.