Changeset 10339:01a949be2367 in orange


Ignore:
Timestamp:
02/22/12 14:26:16 (2 years ago)
Author:
Lan Zagar <lan.zagar@…>
Branch:
default
rebase_source:
78096b8768302f3c253eb08a3a8bbb3b4af01cca
Message:

Added multitarget scoring to documentation.

Files:
4 edited

Legend:

Unmodified
Added
Removed
  • Orange/evaluation/scoring.py

    r10285 r10339  
    25392539 
    25402540 
    2541 def mt_average_scores(res, score, weights=None): 
     2541def mt_average_score(res, score, weights=None): 
    25422542    """ 
    25432543    Average the scores of individual targets. 
     
    25732573def mt_flattened_score(res, score): 
    25742574    """ 
    2575     Flatten the predictions of multiple targets 
    2576     and compute a single-target score. 
     2575    Flatten (concatenate into a single list) the predictions of multiple 
     2576    targets and compute a single-target score. 
    25772577     
    25782578    :param score: Single-target scoring method. 
  • docs/reference/rst/Orange.evaluation.scoring.rst

    r10282 r10339  
    127127.. autofunction:: split_by_iterations 
    128128 
    129 ===================================== 
    130 Scoring for multilabel classification 
    131 ===================================== 
     129 
     130.. _mt-scoring: 
     131 
     132============ 
     133Multi-target 
     134============ 
     135 
     136:doc:`Multi-target <Orange.multitarget>` classifiers predict values for 
     137multiple target classes. They can be used with standard 
     138:obj:`~Orange.evaluation.testing` procedures (e.g. 
     139:obj:`~Orange.evaluation.testing.Evaluation.cross_validation`), but require special 
     140scoring functions to compute a single score from the obtained 
     141:obj:`~Orange.evaluation.testing.ExperimentResults`. 
     142 
     143.. autofunction:: mt_flattened_score 
     144.. autofunction:: mt_average_score 
     145 
     146========================== 
     147Multi-label classification 
     148========================== 
    132149 
    133150Multi-label classification requires different metrics than those used in 
  • docs/reference/rst/Orange.evaluation.testing.rst

    r10192 r10339  
    1515 
    1616Different evaluation techniques are implemented as instance methods of 
    17 :obj:`Evaluation` class. For ease of use, an instance of this class in 
     17:obj:`Evaluation` class. For ease of use, an instance of this class is 
    1818created at module loading time and instance methods are exposed as functions 
    1919with the same name in Orange.evaluation.testing namespace. 
  • docs/reference/rst/Orange.multitarget.rst

    r10332 r10339  
    44 
    55Multi-target prediction tries to achieve better prediction accuracy or speed 
    6 through prediction of multiple dependent variable at once. It works on 
     6through prediction of multiple dependent variables at once. It works on 
    77:ref:`multi-target data <multiple-classes>`, which is also supported by 
    88Orange's tab file format using :ref:`multiclass directive <tab-delimited>`. 
     
    1515   Orange.regression.earth 
    1616 
     17For evaluation of multi-target methods, see the corresponding section in  
     18:ref:`Orange.evaluation.scoring <mt-scoring>`. 
     19 
    1720 
    1821.. automodule:: Orange.multitarget 
     22 
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