source: orange/docs/widgets/rst/classify/logisticregression.rst @ 11050:e3c4699ca155

Revision 11050:e3c4699ca155, 2.6 KB checked in by Miha Stajdohar <miha.stajdohar@…>, 16 months ago (diff)

Widget docs From HTML to Sphinx.

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 in the dialog.
24
25   - Logistic Regression Classifier
26      Trained classifier (a subtype of Classifier)
27
28
29Signal :code:`Logistic Regression Classifier` sends data only if the learning data (signal :code:`Examples` is present.
30
31Description
32-----------
33
34This widget provides a graphical interface to the logistic regression classifier.
35
36As all widgets for classification, this widget provides a learner and classifier on the output. Learner is a learning algorithm with settings as specified by the user. It can be fed into widgets for testing learners, for instance :code:`Test Learners`. Classifier is a logistic regression classifier (a subtype of a general classifier), built from the training examples on the input. If examples are not given, there is no classifier on the output.
37
38The widget requires - due to limitations of the learning algorithm - data with binary class.
39
40.. image:: images/LogisticRegression.png
41   :alt: Logistic Regression Widget
42
43Learner can be given a name under which it will appear in, say, :code:`Test Learners`. The default name is "Logistic Regression".
44
45If :obj:`Stepwise attribute selection` is checked, the learner will iteratively add and remove the attributes, one at a time, based on their significance. The thresholds for addition and removal of the attribute are set in :obj:`Add threshold` and :obj:`Remove threshold`. It is also possible to limit the total number of attributes in the model.
46
47Independent of these settings, the learner will always remove singular attributes, for instance the constant attributes or those which can be expressed as a linear combination of other attributes.
48
49Logistic regression has no internal mechanism for dealing with missing values. These thus need to be imputed. The widget offers a number of options: it can impute the average value of the attribute, its minimum and maximum or train a model to predict the attribute's values based on values of other attributes. It can also remove the examples with missing values.
50
51Note that there also exist a separate widget for missing data imputation, `Impute <../Data/Impute.htm>`_.
52
53
54Examples
55--------
56
57The widget is used just as any other widget for inducing classifier. See, for instance, the example for the `Naive Bayesian Classifier <NaiveBayes.htm>`_.
Note: See TracBrowser for help on using the repository browser.