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orange-reliability / docs / rst / Orange.evaluation.reliability.rst

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docs/rst/Orange.evaluation.reliability.rst

 ********************************************************
 
 Reliability assessment aims to predict reliabilities of individual
-predictions. 
-
-Most of implemented algorithms for regression described in
-"Comparison of approaches for estimating reliability of individual
-regression predictions, Zoran Bosnić, 2008" for regression and in
-in "Evaluating Reliability of Single
-Classifications of Neural Networks, Darko Pevec, 2011" for classification.
+predictions. Most of implemented algorithms for regression described in
+[Bosnic2008]_ and in [Pevec2011]_ for classification.
 
 We can use reliability estimation with any Orange learners. The following example:
 
  * Constructs reliability estimators (implemented in this module),
- * Combines a regular learner.
-   (:class:`~Orange.classification.knn.kNNLearner` in this case) with
-   reliability estimators.
+ * The :obj:`Learner` wrapper combines a regular learner, here a :obj:`~Orange.classification.knn.kNNLearner`, with reliability estimators.
  * Obtains prediction probabilities from the constructed classifier
    (:obj:`Orange.classification.Classifier.GetBoth` option). The resulting
-   probabilities have and additional attribute, :obj:`reliability_estimate`
-   attribute, :class:`Orange.evaluation.reliability.Estimate`.
+   probabilities have an additional attribute, :obj:`reliability_estimate`,
+   that contains a list of :class:`Orange.evaluation.reliability.Estimate`.
 
 .. literalinclude:: code/reliability-basic.py
     :lines: 7-
 .. literalinclude:: code/reliability-run.py
     :lines: 7-
 
+Reliability estimation wrappers
+===============================
+
+.. autoclass:: Learner
+   :members: __call__
+
+.. autoclass:: Classifier
+   :members: __call__
+
+
 Reliability Methods
 ===================
 
-For regression, you can use all the described measures except :math:`O_{ref}`. Classification is
+All measures except :math:`O_{ref}` work with regression. Classification is
 supported by BAGV, LCV, CNK and DENS, :math:`O_{ref}`.
 
 Sensitivity Analysis (SAvar and SAbias)
 
 
 Stacked generalization (Stacking)
--------------------------------
+---------------------------------
 
 .. autoclass:: Stacking
 
 
 .. autoclass:: ReferenceExpectedError
 
-Reliability estimation wrappers
-===============================
-
-.. autoclass:: Learner(box_learner, name="Reliability estimation", estimators=[SensitivityAnalysis(), LocalCrossValidation(), BaggingVarianceCNeighbours(), Mahalanobis(), MahalanobisToCenter()], **kwds)
-    :members:
-
-.. autoclass:: Classifier
-    :members:
-
 Reliability estimation results
 ==============================
 
 References
 ==========
 
-Bosnić, Z., Kononenko, I. (2007) `Estimation of individual prediction
-reliability using local sensitivity analysis. <http://www.springerlink
-.com/content/e27p2584387532g8/>`_ *Applied Intelligence* 29(3), pp. 187-203.
+.. [Bosnic2007]  Bosnić, Z., Kononenko, I. (2007) `Estimation of individual prediction reliability using local sensitivity analysis. <http://www.springerlink.com/content/e27p2584387532g8/>`_ *Applied Intelligence* 29(3), pp. 187-203.
 
-Bosnić, Z., Kononenko, I. (2008) `Comparison of approaches for estimating
-reliability of individual regression predictions. <http://www.sciencedirect
-.com/science/article/pii/S0169023X08001080>`_ *Data & Knowledge Engineering*
-67(3), pp. 504-516.
+.. [Bosnic2008] Bosnić, Z., Kononenko, I. (2008) `Comparison of approaches for estimating reliability of individual regression predictions. <http://www.sciencedirect .com/science/article/pii/S0169023X08001080>`_ *Data & Knowledge Engineering* 67(3), pp. 504-516.
 
-Bosnić, Z., Kononenko, I. (2010) `Automatic selection of reliability estimates
-for individual regression predictions. <http://journals.cambridge
-.org/abstract_S0269888909990154>`_ *The Knowledge Engineering Review* 25(1),
-pp. 27-47.
+.. [Bosnic2010] Bosnić, Z., Kononenko, I. (2010) `Automatic selection of reliability estimates for individual regression predictions. <http://journals.cambridge .org/abstract_S0269888909990154>`_ *The Knowledge Engineering Review* 25(1), pp. 27-47.
 
-Pevec, D., Štrumbelj, E., Kononenko, I. (2011) `Evaluating Reliability of
-Single Classifications of Neural Networks. <http://www.springerlink.com
-/content/48u881761h127r33/export-citation/>`_ *Adaptive and Natural Computing
-Algorithms*, 2011, pp. 22-30.
+.. [Pevec2011] Pevec, D., Štrumbelj, E., Kononenko, I. (2011) `Evaluating Reliability of Single Classifications of Neural Networks. <http://www.springerlink.com /content/48u881761h127r33/export-citation/>`_ *Adaptive and Natural Computing Algorithms*, 2011, pp. 22-30.