Uses of Class
com.rapidminer.tools.math.Averagable

Packages that use Averagable

com.rapidminer.example	The data core classes of RapidMiner.
com.rapidminer.operator.performance	Provides performance evaluating operators and performance criteria.
com.rapidminer.operator.performance.cost	This package contains cost-based performance evaluations.
com.rapidminer.tools.math	Several tool classes for mathematical operations.

Uses of Averagable in com.rapidminer.example

Subclasses of Averagable in com.rapidminer.example

class

AttributeWeight Helper class containing the name of an attribute and the corresponding weight.

Methods in com.rapidminer.example with parameters of type Averagable

void	AttributeWeight.buildSingleAverage(Averagable avg) Builds the sum of weights and counters.

Uses of Averagable in com.rapidminer.operator.performance

Subclasses of Averagable in com.rapidminer.operator.performance

class	AbsoluteError The absolute error: Sum(|label-predicted|)/#examples.
class	AreaUnderCurve This criterion calculates the area under the ROC curve.
class	BinaryClassificationPerformance This class encapsulates the well known binary classification criteria precision and recall.
class	CorrelationCriterion Computes the empirical corelation coefficient 'r' between label and prediction.
class	CrossEntropy Calculates the cross-entropy for the predictions of a classifier.
class	EstimatedPerformance This class is used to store estimated performance values before or even without the performance test is actually done using a test set.
class	LenientRelativeError The average relative error in a lenient way of calculation: Sum(|label-predicted|/max(|label|, |predicted|))/#examples.
class	LogisticLoss The logistic loss of a classifier, defined as the average over all ln(1 + exp(-y * f(x)))
class	Margin The margin of a classifier, defined as the minimal confidence for the correct label.
class	MDLCriterion Measures the length of an example set (i.e. the number of attributes).
class	MeasuredPerformance Superclass for performance citeria that are actually measured (not estimated).
class	MinMaxCriterion This criterion should be used as wrapper around other performance criteria (see MinMaxWrapper).
class	MultiClassificationPerformance Measures the accuracy and classification error for both binary classification problems and multi class problems.
class	NormalizedAbsoluteError Normalized absolute error is the total absolute error normalized by the error simply predicting the average of the actual values.
class	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.
class	PredictionAverage Returns the average value of the prediction.
class	PredictionTrendAccuracy Measures the number of times a regression prediction correctly determines the trend.
class	RankCorrelation Computes either the Spearman (rho) or Kendall (tau-b) rank correlation between the actual label and predicted values of an example set.
class	RelativeError The average relative error: Sum(|label-predicted|/label)/#examples.
class	RootMeanSquaredError The root-mean-squared error.
class	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.
class	SimpleClassificationError This class calculates the classification error without determining the complete contingency table.
class	SimpleCriterion Simple criteria are those which error can be counted for each example and can be averaged by the number of examples.
class	SoftMarginLoss The soft margin loss of a classifier, defined as the average over all 1 - y * f(x).
class	SquaredCorrelationCriterion Computes the square of the empirical corellation coefficient 'r' between label and prediction.
class	SquaredError The squared error.
class	StrictRelativeError The average relative error in a strict way of calculation: Sum(|label-predicted|/min(|label|, |predicted|))/#examples.
class	WeightedMultiClassPerformance Measures the weighted mean of all per class recalls or per class precisions based on the weights defined in the performance evaluator.

Methods in com.rapidminer.operator.performance with parameters of type Averagable

void	WeightedMultiClassPerformance.buildSingleAverage(Averagable performance)
void	SoftMarginLoss.buildSingleAverage(Averagable performance)
void	SimpleCriterion.buildSingleAverage(Averagable performance)
void	RootRelativeSquaredError.buildSingleAverage(Averagable performance)
protected void	RankCorrelation.buildSingleAverage(Averagable averagable) Averaging across instances of RankCorrelation is unsupported (?)
void	PredictionTrendAccuracy.buildSingleAverage(Averagable averagable)
void	PredictionAverage.buildSingleAverage(Averagable performance)
void	NormalizedAbsoluteError.buildSingleAverage(Averagable performance)
void	MultiClassificationPerformance.buildSingleAverage(Averagable performance)
void	MinMaxCriterion.buildSingleAverage(Averagable avg)
void	MDLCriterion.buildSingleAverage(Averagable averagable)
void	Margin.buildSingleAverage(Averagable performance)
void	LogisticLoss.buildSingleAverage(Averagable performance)
void	EstimatedPerformance.buildSingleAverage(Averagable performance)
void	CrossEntropy.buildSingleAverage(Averagable performance)
void	CorrelationCriterion.buildSingleAverage(Averagable performance)
void	BinaryClassificationPerformance.buildSingleAverage(Averagable performance)
void	AreaUnderCurve.buildSingleAverage(Averagable performance)

Uses of Averagable in com.rapidminer.operator.performance.cost

Subclasses of Averagable in com.rapidminer.operator.performance.cost

class

ClassificationCostCriterion This performance Criterion works with a given cost matrix.

Methods in com.rapidminer.operator.performance.cost with parameters of type Averagable

protected void

ClassificationCostCriterion.buildSingleAverage(Averagable averagable)

Uses of Averagable in com.rapidminer.tools.math

Methods in com.rapidminer.tools.math that return Averagable

Averagable	AverageVector.getAveragable(int index) Returns the Averagable by index.
Averagable	AverageVector.getAveragable(java.lang.String name) Returns the Averagable by name.

Methods in com.rapidminer.tools.math with parameters of type Averagable

void	AverageVector.addAveragable(Averagable avg) Adds an Averagable to the list of criteria.
void	Averagable.buildAverage(Averagable averagable) This method builds the makro average of two averagables of the same type.
protected abstract void	Averagable.buildSingleAverage(Averagable averagable) This method should build the average of this and another averagable of the same type.
protected void	Averagable.cloneAveragable(Averagable other) Deprecated. Please use copy constructors instead
void	AverageVector.removeAveragable(Averagable avg) Removes an Averagable from the list of criteria.

Constructors in com.rapidminer.tools.math with parameters of type Averagable

Averagable(Averagable o)