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