Package adaa.analytics.rules.logic.performance

package adaa.analytics.rules.logic.performance

Classes

Class	Description
AbstractPerformanceCounter
BalancedAccuracyPerformance	Class gathering additional performance measures for balanced accuracy.
BinaryClassificationPerformance	This class encapsulates the well known binary classification criteria precision and recall.
ClassificationRulesPerformance	Class gathering additional performance measures for classification models (avg.
CorrelationCriterion	Computes the empirical corelation coefficient 'r' between label and prediction.
IntegratedBrierScore	Class representing integrated Brioer score - a performance measures for survival models.
MultiClassificationPerformance	Measures the accuracy and classification error for both binary classification problems and multi class problems.
NegativeVotingConflictsPerformance	Class gathering number of negative voting conflicts).
NormalizedAbsoluteError	Normalized absolute error is the total absolute error normalized by the error simply predicting the average of the actual values.
PerformanceResult
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.
RulePerformanceCounter
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.