Changeset 5:f50b356a019c in orange-reliability for docs/rst/Orange.evaluation.reliability.rst


Ignore:
Timestamp:
06/09/12 13:54:12 (23 months ago)
Author:
Matija Polajnar <matija.polajnar@…>
Branch:
default
Message:

Merge in Lan Umek's implementations of reliability estimation for classification.

File:
1 edited

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  • docs/rst/Orange.evaluation.reliability.rst

    r4 r5  
    1515 
    1616Reliability assessment statistically predicts reliability of single 
    17 predictions. Most of implemented algorithms are taken from Comparison of 
    18 approaches for estimating reliability of individual regression predictions, 
    19 Zoran Bosnić, 2008. 
     17predictions. Most of implemented algorithms for regression are taken from 
     18Comparison of approaches for estimating reliability of individual 
     19regression predictions, Zoran Bosnić, 2008. Implementations for 
     20classification follow descriptions in Evaluating Reliability of Single 
     21Classifications of Neural Networks, Darko Pevec, 2011. 
    2022 
    2123The following example shows basic usage of reliability estimation methods: 
     
    4648Reliability Methods 
    4749=================== 
     50 
     51For regression, all the described measures can be used. Classification domains 
     52are supported by the following methods: BAGV, LCV, CNK and DENS. 
    4853 
    4954Sensitivity Analysis (SAvar and SAbias) 
     
    7782 
    7883.. autoclass:: MahalanobisToCenter 
     84 
     85Density estimation using Parzen window (DENS) 
     86--------------------------------------------- 
     87 
     88.. autoclass:: ParzenWindowDensityBased 
    7989 
    8090Reliability estimation wrappers 
     
    151161pp. 27-47. 
    152162 
     163Pevec, D., Štrumbelj, E., Kononenko, I. (2011) `Evaluating Reliability of 
     164Single Classifications of Neural Networks. <http://www.springerlink.com 
     165/content/48u881761h127r33/export-citation/>`_ *Adaptive and Natural Computing 
     166Algorithms*, 2011, pp. 22-30. 
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