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/logisticregression.rst

    r11050 r11359  
    2121 
    2222   - Learner 
    23       The logistic regression learning algorithm with settings as specified in the dialog. 
     23      The logistic regression learning algorithm with settings as specified 
     24      in the dialog. 
    2425 
    2526   - Logistic Regression Classifier 
     
    2728 
    2829 
    29 Signal :code:`Logistic Regression Classifier` sends data only if the learning data (signal :code:`Examples` is present. 
     30Signal :code:`Logistic Regression Classifier` sends data only if the learning 
     31data (signal :code:`Examples` is present. 
    3032 
    3133Description 
    3234----------- 
    3335 
    34 This widget provides a graphical interface to the logistic regression classifier. 
     36This widget provides a graphical interface to the logistic regression 
     37classifier. 
    3538 
    36 As 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. 
     39As all widgets for classification, this widget provides a learner and 
     40classifier on the output. Learner is a learning algorithm with settings 
     41as specified by the user. It can be fed into widgets for testing learners, 
     42for instance :ref:`Test Learners`. Classifier is a logistic regression 
     43classifier (a subtype of a general classifier), built from the training 
     44examples on the input. If examples are not given, there is no classifier 
     45on the output. 
    3746 
    38 The widget requires - due to limitations of the learning algorithm - data with binary class. 
     47The widget requires - due to limitations of the learning algorithm - data with 
     48binary class. 
    3949 
    4050.. image:: images/LogisticRegression.png 
    4151   :alt: Logistic Regression Widget 
    4252 
    43 Learner can be given a name under which it will appear in, say, :code:`Test Learners`. The default name is "Logistic Regression". 
     53Learner can be given a name under which it will appear in, say, 
     54:ref:`Test Learners`. The default name is "Logistic Regression". 
    4455 
    45 If :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. 
     56If :obj:`Stepwise attribute selection` is checked, the learner will 
     57iteratively add and remove the attributes, one at a time, based on their 
     58significance. The thresholds for addition and removal of the attribute are 
     59set in :obj:`Add threshold` and :obj:`Remove threshold`. It is also possible 
     60to limit the total number of attributes in the model. 
    4661 
    47 Independent 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. 
     62Independent of these settings, the learner will always remove singular 
     63attributes, for instance the constant attributes or those which can be 
     64expressed as a linear combination of other attributes. 
    4865 
    49 Logistic 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. 
     66Logistic regression has no internal mechanism for dealing with missing 
     67values. These thus need to be imputed. The widget offers a number of options: 
     68it can impute the average value of the attribute, its minimum and maximum or 
     69train a model to predict the attribute's values based on values of other 
     70attributes. It can also remove the examples with missing values. 
    5071 
    51 Note that there also exist a separate widget for missing data imputation, `Impute <../Data/Impute.htm>`_. 
     72Note that there also exist a separate widget for missing data imputation, 
     73:ref:`Impute`. 
    5274 
    5375 
     
    5577-------- 
    5678 
    57 The widget is used just as any other widget for inducing classifier. See, for instance, the example for the `Naive Bayesian Classifier <NaiveBayes.htm>`_. 
     79The widget is used just as any other widget for inducing classifier. See, 
     80for instance, the example for the :ref:`Naive Bayes`. 
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