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