R/MLPUGS.R

In MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

#' @title MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)
#' @description An implementation of classifier chains for binary and probabilistic
#'   multi-label prediction. The classification pipeline consists of:
#'   \enumerate{
#'     \item Training an ensemble of classifier chains. Each chain is a binary
#'           classifier (built-in, supplied from an external package or user-coded).
#'     \item Making predictions using a Gibbs sampler since each unobserved
#'           label is conditioned on the others.
#'     \item (Optional) Evaluating the ECC.
#'     \item Gathering predictions (aggregating across iterations & models).
#'   }
#'   To learn more about MLPUGS, start with the vignettes: \code{browseVignettes(package = "MLPUGS")}
#' @aliases MLPUGS
#' @docType package
#' @name MLPUGS-package
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