Package edu.udo.cs.yale.operator.performance

Provides performance evaluating operators and performance criteria.

See:
Description

Interface Summary

PerformanceComparator

Compares two PerformanceVectors.

Class Summary

AbsoluteError	The absolute error: Sum(|label-predicted|)/#examples.
AreaUnderCurve	This criterion calculates the area under the ROC curve.
AttributeCounter	Returns a performance vector just counting the number of attributes currently used for the given example set.
BinaryClassificationPerformance	This class encapsulates the well known binary classification criteria precision and recall.
CorrelationCriterion	Computes the empirical corelation coefficient 'r' between label and prediction.
EstimatedPerformance	This class is used to store estimated performance values before or even without the performance test is actually done using a test set.
Margin	The margin of a classifier, defined as the minimal confidence for the correct label.
MDLCriterion	Measures the length of an example set (i.e. the number of attributes).
MeasuredPerformance	Superclass for performance citeria that are actually measured (not estimated).
MinMaxCriterion	This criterion should be used as wrapper around other performance criteria (see MinMaxWrapper).
MinMaxWrapper	Wraps a MinMaxCriterion around each performance criterion of type MeasuredPerformance.
MultiClassificationPerformance	Measures the accuracy and classification error for both binary classification problems and multi class problems.
NormalizedAbsoluteError	Normalized absolute error is the total absolute error normalized by the error simply predicting average of the actual values.
PerformanceCriterion	Each PerformanceCriterion contains a method to compute this criterion on a given set of examples, each which has to have a real and a predicted label.
PerformanceEvaluator	A performance evaluator is an operator that expects a test ExampleSet as input, whose elements have both true and predicted labels, and delivers as output a list of performance values according to a list of performance criteria that it calculates.
PerformanceVector	Handles several performance criteria.
PerformanceVector.DefaultComparator	The default performance comparator compares the main criterion of two performance vectors.
PredictionAverage	Returns the average value of the prediction.
RelativeError	The average relative error: Sum(|label-predicted|/label)/#examples.
RootMeanSquaredError	The root-mean-squared error.
RootRelativeSquaredError	Relative squared error is the total squared error made relative to what the error would have been if the prediction had been the average of the absolute value.
SimpleAccuracy	This class calculates the accuracy without determining the complete contingency table.
SimpleCriterion	Simple criteria are those which error can be counted for each example and can be averaged by the number of examples.
SquaredCorrelationCriterion	Computes the square of the empirical corellation coefficient 'r' between label and prediction.
SquaredError	The squared error.
WeightedPerformanceCreator	Returns a performance vector containing the weighted fitness value of the input criteria.

Package edu.udo.cs.yale.operator.performance Description

Provides performance evaluating operators and performance criteria.