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

Revision 11404:1a7b773d7c7b, 3.0 KB checked in by Ales Erjavec <ales.erjavec@…>, 13 months ago (diff)

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

Line 
1.. _Naive Bayes:
2
3Naive Bayesian Learner
4======================
5
6.. image:: ../icons/NaiveBayes.png
7
8Naive Bayesian 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 naive Bayesian learning algorithm with settings as specified in
24      the dialog.
25
26   - Naive Bayesian Classifier
27      Trained classifier (a subtype of Classifier)
28
29
30Signal :obj:`Naive Bayesian 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 Naive Bayesian classifier.
37
38As all widgets for classification, this widget provides a learner and
39classifier on the output. Learner is a learning algorithm with settings
40as specified by the user. It can be fed into widgets for testing learners,
41for instance :ref:`Test Learners`. Classifier is a Naive Bayesian Classifier
42(a subtype of a general classifier), built from the training examples on the
43input. If examples are not given, there is no classifier on the output.
44
45.. image:: images/NaiveBayes.png
46   :alt: NaiveBayes Widget
47
48Learner can be given a name under which it will appear in, say,
49:ref:`Test Learners`. The default name is "Naive Bayes".
50
51Next come the probability estimators. :obj:`Prior` sets the method used for
52estimating prior class probabilities from the data. You can use either
53:obj:`Relative frequency` or the :obj:`Laplace estimate`.
54:obj:`Conditional (for discrete)` sets the method for estimating conditional
55probabilities, besides the above two, conditional probabilities can be
56estimated using the :obj:`m-estimate`; in this case the value of m should be
57given as the :obj:`Parameter for m-estimate`. By setting it to
58:obj:`<same as above>` the classifier will use the same method as for
59estimating prior probabilities.
60
61Conditional probabilities for continuous attributes are estimated using
62LOESS. :obj:`Size of LOESS window` sets the proportion of points in the
63window; higher numbers mean more smoothing.
64:obj:`LOESS sample points` sets the number of points in which the function
65is sampled.
66
67If the class is binary, the classification accuracy may be increased
68considerably by letting the learner find the optimal classification
69threshold (option :obj:`Adjust threshold`). The threshold is computed from
70the training data. If left unchecked, the usual threshold of 0.5 is used.
71
72When you change one or more settings, you need to push :obj:`Apply`;
73this will put the new learner on the output and, if the training examples
74are given, construct a new classifier and output it as well.
75
76
77Examples
78--------
79
80There are two typical uses of this widget. First, you may want to induce
81the model and check what it looks like in a :ref:`Nomogram`.
82
83.. image:: images/NaiveBayes-SchemaClassifier.png
84   :alt: Naive Bayesian Classifier - Schema with a Classifier
85
86The second schema compares the results of Naive Bayesian learner with
87another learner, a C4.5 tree.
88
89.. image:: images/C4.5-SchemaLearner.png
90   :alt: Naive Bayesian Classifier - Schema with a Learner
Note: See TracBrowser for help on using the repository browser.