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orange/docs/widgets/rst/classify/naivebayes.rst
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[11050] | 1 | .. _Naive Bayes: |

2 | ||

3 | Naive Bayesian Learner | |

4 | ====================== | |

5 | ||

6 | .. image:: ../icons/NaiveBayes.png | |

7 | ||

8 | Naive Bayesian Learner | |

9 | ||

10 | Signals | |

11 | ------- | |

12 | ||

13 | Inputs: | |

14 | ||

15 | ||

16 | - Examples (ExampleTable) | |

17 | A table with training examples | |

18 | ||

19 | ||

20 | Outputs: | |

21 | ||

22 | - Learner | |

[11359] | 23 | The naive Bayesian learning algorithm with settings as specified in |

24 | the dialog. | |

[11050] | 25 | |

26 | - Naive Bayesian Classifier | |

27 | Trained classifier (a subtype of Classifier) | |

28 | ||

29 | ||

[11404] | 30 | Signal :obj:`Naive Bayesian Classifier` sends data only if the learning |

31 | data (signal :obj:`Examples` is present. | |

[11050] | 32 | |

33 | Description | |

34 | ----------- | |

35 | ||

36 | This widget provides a graphical interface to the Naive Bayesian classifier. | |

37 | ||

[11359] | 38 | As all widgets for classification, this widget provides a learner and |

39 | classifier on the output. Learner is a learning algorithm with settings | |

40 | as specified by the user. It can be fed into widgets for testing learners, | |

41 | for instance :ref:`Test Learners`. Classifier is a Naive Bayesian Classifier | |

42 | (a subtype of a general classifier), built from the training examples on the | |

43 | input. If examples are not given, there is no classifier on the output. | |

[11050] | 44 | |

45 | .. image:: images/NaiveBayes.png | |

46 | :alt: NaiveBayes Widget | |

47 | ||

[11359] | 48 | Learner can be given a name under which it will appear in, say, |

49 | :ref:`Test Learners`. The default name is "Naive Bayes". | |

[11050] | 50 | |

[11359] | 51 | Next come the probability estimators. :obj:`Prior` sets the method used for |

52 | estimating 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 | |

55 | probabilities, besides the above two, conditional probabilities can be | |

56 | estimated using the :obj:`m-estimate`; in this case the value of m should be | |

57 | given 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 | |

59 | estimating prior probabilities. | |

[11050] | 60 | |

[11359] | 61 | Conditional probabilities for continuous attributes are estimated using |

62 | LOESS. :obj:`Size of LOESS window` sets the proportion of points in the | |

63 | window; higher numbers mean more smoothing. | |

64 | :obj:`LOESS sample points` sets the number of points in which the function | |

65 | is sampled. | |

[11050] | 66 | |

[11359] | 67 | If the class is binary, the classification accuracy may be increased |

68 | considerably by letting the learner find the optimal classification | |

69 | threshold (option :obj:`Adjust threshold`). The threshold is computed from | |

70 | the training data. If left unchecked, the usual threshold of 0.5 is used. | |

[11050] | 71 | |

[11359] | 72 | When you change one or more settings, you need to push :obj:`Apply`; |

73 | this will put the new learner on the output and, if the training examples | |

74 | are given, construct a new classifier and output it as well. | |

[11050] | 75 | |

76 | ||

77 | Examples | |

78 | -------- | |

79 | ||

[11359] | 80 | There are two typical uses of this widget. First, you may want to induce |

81 | the model and check what it looks like in a :ref:`Nomogram`. | |

[11050] | 82 | |

83 | .. image:: images/NaiveBayes-SchemaClassifier.png | |

84 | :alt: Naive Bayesian Classifier - Schema with a Classifier | |

85 | ||

[11359] | 86 | The second schema compares the results of Naive Bayesian learner with |

87 | another learner, a C4.5 tree. | |

[11050] | 88 | |

89 | .. image:: images/C4.5-SchemaLearner.png | |

90 | :alt: Naive Bayesian Classifier - Schema with a Learner |

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