predictClassViaVoting: Predict a class using (un)weighted voting.

In boostr: A modular framework to bag or boost any estimation procedure.

Description

Process a matrix of class predictions and form a column-wise estimate based on weighted voting.

Usage

predictClassFromWeightedVote(preds, weights, .parallel = FALSE,
  .rngSeed = 1234)

predictClassFromVote(preds, .parallel = FALSE, .rngSeed = 1234)

Arguments

preds	is (character) matrix of predicted classes
weights	is a vector of length equal to nrow(preds)
.parallel	is a boolean flag determining whether to work across columns of preds in parallel – need to register a parallel backend (e.g. doParallel, doRedis) for this to actually work.
.rngSeed	the value of the RNG seed to be used in the case that ties are to be randomly broken.

Details

Gives the vote from row(i) in preds weight equal to weights[i]. Ties are broken randomly, but before so, the seed is set to .rngSeed.

Value

a character vector of length equal to ncol(preds) containing the class estimates per column of preds.

boostr documentation built on May 2, 2019, 1:42 p.m.