Ignore:
Timestamp:
02/27/13 15:02:50 (14 months ago)
Author:
Ales Erjavec <ales.erjavec@…>
Branch:
default
Message:

Cleanup of 'Widget catalog' documentation.

Fixed rst text formating, replaced dead hardcoded reference links (now using
:ref:), etc.

File:
1 edited

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  • docs/widgets/rst/classify/c45.rst

    r11050 r11359  
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    35 :code:`Classifier`, :code:`C45 Tree` and :code:`Classification Tree` are available only if examples are present on the input. Which of the latter two output signals is active is determined by setting :obj:`Convert to orange tree structure` (see the description below. 
     35:code:`Classifier`, :code:`C45 Tree` and :code:`Classification Tree` are 
     36available only if examples are present on the input. Which of the latter two 
     37output signals is active is determined by setting 
     38:obj:`Convert to orange tree structure` (see the description below). 
    3639 
    3740Description 
    3841----------- 
    3942 
    40 This widget provides a graphical interface to the well-known Quinlan's C4.5 algorithm for construction of classification tree. Orange uses the original Quinlan's code which must be, due to copyright issues, built and linked in separately. 
     43This widget provides a graphical interface to the well-known Quinlan's C4.5 
     44algorithm for construction of classification tree. Orange uses the original 
     45Quinlan's code which must be, due to copyright issues, built and linked in 
     46separately. 
    4147 
    42 Orange also implements its own classification tree induction algorithm which is comparable to Quinlan's, though the results may differ due to technical details. It is accessible in widget :code:`Classification Tree`. 
     48Orange also implements its own classification tree induction algorithm which 
     49is comparable to Quinlan's, though the results may differ due to technical 
     50details. It is accessible in widget :ref:`Classification Tree`. 
    4351 
    44 As all widgets for classification, C4.5 widget provides 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, namely :code:`Test Learners`. Classifier is a classification tree build from the training examples on the input. If examples are not given, the widget outputs no classifier. 
     52As all widgets for classification, C4.5 widget provides learner and classifier 
     53on the output. Learner is a learning algorithm with settings as specified by 
     54the user. It can be fed into widgets for testing learners, namely 
     55:ref:`Test Learners`. Classifier is a classification tree build from the 
     56training examples on the input. If examples are not given, the widget outputs 
     57no classifier. 
    4558 
    4659.. image:: images/C4.5.png 
    4760   :alt: C4.5 Widget 
    4861 
    49 Learner can be given a name under which it will appear in, say, :code:`Test Learners`. The default name is "C4.5". 
     62Learner can be given a name under which it will appear in, say, 
     63:ref:`Test Learners`. The default name is "C4.5". 
    5064 
    51 The next block of options deals with splitting. C4.5 uses gain ratio by default; to override this, check :obj:`Use information gain instead of ratio`, which is equivalent to C4.5's command line option :code:`-g`. If you enable :obj:`subsetting` (equivalent to :code:`-s`), C4.5 will merge values of multivalued discrete attributes instead of creating one branch for each node. :obj:`Probabilistic threshold for continuous attributes` (:code:`-p`) makes C4.5 compute the lower and upper boundaries for values of continuous attributes for which the number of misclassified examples would be within one standard deviation from the base error. 
     65The next block of options deals with splitting. C4.5 uses gain ratio by 
     66default; to override this, check :obj:`Use information gain instead of ratio`, 
     67which is equivalent to C4.5's command line option :code:`-g`. If you enable 
     68:obj:`subsetting` (equivalent to :code:`-s`), C4.5 will merge values of 
     69multivalued discrete attributes instead of creating one branch for each node. 
     70:obj:`Probabilistic threshold for continuous attributes` (:code:`-p`) makes 
     71C4.5 compute the lower and upper boundaries for values of continuous attributes 
     72for which the number of misclassified examples would be within one standard 
     73deviation from the base error. 
    5274 
    53 As for pruning, you can set the :obj:`Minimal number of examples in the leaves` (Quinlan's default is 2, but you may want to disable this for noiseless data), and the :obj:`Post prunning with confidence level`; the default confidence is 25. 
     75As for pruning, you can set the :obj:`Minimal number of examples in the leaves` 
     76(Quinlan's default is 2, but you may want to disable this for noiseless data), 
     77and the :obj:`Post prunning with confidence level`; the default confidence is 
     7825. 
    5479 
    55 Trees can be constructed iteratively, with ever larger number of examples. If enable, you can set the :obj:`Number of trials`, the :obj:`initial windows size` and :obj:`window increment`. 
     80Trees can be constructed iteratively, with ever larger number of examples. If 
     81enable, you can set the :obj:`Number of trials`, the 
     82:obj:`initial windows size` and :obj:`window increment`. 
    5683 
    57 The resulting classifier can be left in the original Quinlan's structure, as returned by his underlying code, or :obj:`converted to orange the structure` that is used by Orange's tree induction algorithm. This setting decides which of the two signals that output the tree - :code:`C45 Classifier` or :code:`Tree Classifier` will be active. As Orange's structure is more general and can easily accommodate all the data that C4.5 tree needs for classification, we believe that the converted tree behave exactly the same as the original tree, so the results should not depend on this setting. You should therefore leave it enabled since only the converted trees can be shown in the tree displaying widgets. 
     84The resulting classifier can be left in the original Quinlan's structure, as 
     85returned by his underlying code, or :obj:`converted to orange the structure` 
     86that is used by Orange's tree induction algorithm. This setting decides which 
     87of the two signals that output the tree - :code:`C45 Classifier` or 
     88:code:`Tree Classifier` will be active. As Orange's structure is more general 
     89and can easily accommodate all the data that C4.5 tree needs for 
     90classification, we believe that the converted tree behave exactly the same as 
     91the original tree, so the results should not depend on this setting. You should 
     92therefore leave it enabled since only the converted trees can be shown in the 
     93tree displaying widgets. 
    5894 
    59 When you change one or more settings, you need to push :obj:`Apply`; this will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. 
     95When you change one or more settings, you need to push :obj:`Apply`; this will 
     96put the new learner on the output and, if the training examples are given, 
     97construct a new classifier and output it as well. 
    6098 
    6199 
     
    63101-------- 
    64102 
    65 There are two typical uses of this widget. First, you may want to induce the tree and see what it looks like, like in the schema on the right. 
     103There are two typical uses of this widget. First, you may want to induce the 
     104tree and see what it looks like, like in the schema on the right. 
    66105 
    67106.. image:: images/C4.5-SchemaClassifier2.png 
    68107   :alt: C4.5 - Schema with a Classifier 
    69108 
    70 The second schema shows how to compare the results of C4.5 learner with another classifier, naive Bayesian Learner. 
     109The second schema shows how to compare the results of C4.5 learner with another 
     110classifier, naive Bayesian Learner. 
    71111 
    72112.. image:: images/C4.5-SchemaLearner.png 
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