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docs/rst/Orange.evaluation.reliability.rst

 Reliability Estimation for Regression and Classification
 ********************************************************
 
-Reliability assessment statistically predicts reliability of single
-predictions. Most of implemented algorithms for regression are taken from
-Comparison of approaches for estimating reliability of individual
-regression predictions, Zoran Bosnić, 2008. Implementations for
-classification follow descriptions in Evaluating Reliability of Single
-Classifications of Neural Networks, Darko Pevec, 2011.
+Reliability assessment aims to predict reliabilities of individual
+predictions. 
 
-The following example shows basic usage of reliability estimation methods:
+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.
+
+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.
+ * 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`.
 
 .. literalinclude:: code/reliability-basic.py
     :lines: 7-
 
-The important points of this example are:
- * construction of reliability estimators using classes,
-   implemented in this module,
- * construction of a reliability learner that bonds a regular learner
-   (:class:`~Orange.classification.knn.kNNLearner` in this case) with
-   reliability estimators,
- * calling the constructed classifier with
-   :obj:`Orange.classification.Classifier.GetBoth` option to obtain class
-   probabilities; :obj:`probability` is the object that gets appended the
-   :obj:`reliability_estimate` attribute, an instance of
-   :class:`Orange.evaluation.reliability.Estimate`, by the reliability learner.
-
-It is also possible to do reliability estimation on whole data
-table, not only on single instance. Next example demonstrates usage of a
-cross-validation technique for reliability estimation. Reliability estimations
-for first 10 instances get printed:
+We could also evaluate more examples. The next example prints reliability estimates
+for first 10 instances (with cross-validation):
 
 .. literalinclude:: code/reliability-run.py
     :lines: 7-
 Reliability Methods
 ===================
 
-For regression, all the described measures can be used, except for the :math:`O_{ref}`. Classification domains
-are supported by the following methods: BAGV, LCV, CNK and DENS, :math:`O_{ref}`.
+For regression, you can use all the described measures except :math:`O_{ref}`. Classification is
+supported by BAGV, LCV, CNK and DENS, :math:`O_{ref}`.
 
 Sensitivity Analysis (SAvar and SAbias)
 ---------------------------------------
 Reliability estimation results
 ==============================
 
+.. data:: SIGNED
+    
+.. data:: ABSOLUTE
+
+    These constants distinguish signed and
+    absolute reliability estimation measures.
+
+.. data:: METHOD_NAME
+
+    A dictionary that that maps reliability estimation
+    method IDs (integerss) to method names (strings).
+
 .. autoclass:: Estimate
     :members:
     :show-inheritance:
 
-There is a dictionary named :obj:`METHOD_NAME` that maps reliability estimation
-method IDs (ints) to method names (strings).
 
-In this module, there are also two constants for distinguishing signed and
-absolute reliability estimation measures::
-
-  SIGNED = 0
-  ABSOLUTE = 1
 
 Reliability estimation scoring
 ==============================
 Example
 =======
 
+The following script prints Pearson's correlation coefficient (r) between reliability 
+estimates and actual prediction errors, and a corresponding p-value, for 
+default reliability estimation measures. 
+
 .. literalinclude:: code/reliability-long.py
     :lines: 7-22
 
-This script prints out Pearson's R coefficient between reliability estimates
-and actual prediction errors, and a corresponding p-value, for each of the
-reliability estimation measures used by default. ::
+Results::
   
   Estimate               r       p
   SAvar absolute        -0.077   0.454
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