| ProbAverageVoting {D2MCS} | R Documentation |
Implementation of Probabilistic Average voting.
Description
Computes the final prediction by performing the mean value of the probability achieved by each prediction.
Super class
D2MCS::SimpleVoting -> ProbAverageVoting
Methods
Public methods
Inherited methods
Method new()
Method for initializing the object arguments during runtime.
Usage
ProbAverageVoting$new(cutoff = 0.5, class.tie = NULL, majority.class = NULL)
Arguments
cutoffA character vector defining the minimum probability used to perform a positive classification. If is not defined, 0.5 will be used as default value.
class.tieA character used to define the target class value used when a tie is found. If NULL positive class value will be assigned.
majority.classA character defining the value of the majority class. If NULL will be used same value as training stage.
Method getMajorityClass()
The function returns the value of the majority class.
Usage
ProbAverageVoting$getMajorityClass()
Returns
A character vector of length 1 with the name of the majority class.
Method getClassTie()
The function gets the class value assigned to solve ties.
Usage
ProbAverageVoting$getClassTie()
Returns
A character vector of length 1.
Method execute()
The function implements the majority voting procedure.
Usage
ProbAverageVoting$execute(predictions, verbose = FALSE)
Arguments
predictionsA
ClusterPredictionsobject containing all the predictions achieved for each cluster.verboseA logical value to specify if more verbosity is needed.
Method clone()
The objects of this class are cloneable with this method.
Usage
ProbAverageVoting$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
D2MCS, ClassMajorityVoting,
ClassWeightedVoting, ProbAverageVoting,
ProbAverageWeightedVoting, ProbBasedMethodology