source: orange/docs/reference/rst/Orange.classification.rst @ 9666:41ca9bb351c5

Revision 9666:41ca9bb351c5, 2.1 KB checked in by anze <anze.staric@…>, 2 years ago (diff)

Added documentation for base Learner and Classifier.

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1.. automodule:: Orange.classification
2
3###################################
4Classification (``classification``)
5###################################
6
7All classifiers in Orange consist of two parts, a Learner and a Classifier. A
8learner is constructed with all parameters that will be used for learning.
9When a data table is passed to its __call__ method, a model is fitted to the
10data and return in a form of a Classifier, which is then used for predicting
11the dependent variable(s) of new instances.
12
13.. class:: Learner()
14
15    Base class for all orange learners.
16
17    .. method:: __call__(instances)
18
19        Fit a model and return it as an instance of :class:`Classifier`.
20
21        This method is abstract and needs to be implemented on each learner.
22
23.. class:: Classifier()
24
25    Base class for all orange classifiers.
26
27    .. attribute:: GetValue
28
29        Return value of the target class when performing prediction.
30
31    .. attribute:: GetProbabilities
32
33        Return probability of each target class when performing prediction.
34
35    .. attribute:: GetBoth
36
37        Return a tuple of target class value and probabilities for each class.
38
39
40    .. method:: __call__(instances, return_type)
41
42        Classify a new instance using this model.
43
44        This method is abstract and needs to be implemented on each classifier.
45
46        :param instance: data instance to be classified.
47        :type instance: :class:`~Orange.data.Instance`
48
49        :param return_type: what needs to be predicted
50        :type return_type: :obj:`GetBoth`,
51                           :obj:`GetValue`,
52                           :obj:`GetProbabilities`
53
54        :rtype: :class:`~Orange.data.Value`,
55              :class:`~Orange.statistics.distribution.Distribution` or a
56              tuple with both
57
58Orange contains implementations of various classifiers that are described in
59detail on separate pages.
60
61.. toctree::
62   :maxdepth: 2
63
64   Orange.classification.bayes
65   Orange.classification.knn
66   Orange.classification.logreg
67   Orange.classification.lookup
68   Orange.classification.majority
69   Orange.classification.rules
70   Orange.classification.svm
71   Orange.classification.tree
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