Predict.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Predict.R
\name{Predict}
\alias{Predict}
\title{Compute class predictions for each observation in X}
\usage{
Predict(X, forest, OOB = FALSE, num.cores = 0L, Xtrain = NULL,
  aggregate.output = TRUE, output.scores = FALSE)
}
\arguments{
\item{X}{an n by d numeric matrix (preferable) or data frame. The rows correspond to observations and columns correspond to features of a test set, which should be different from the training set.}

\item{forest}{a forest trained using the RerF function.}

\item{OOB}{if TRUE then run predictions using out-of-bag samples.}

\item{num.cores}{the number of cores to use while training. If NumCores=0 then 1 less than the number of cores reported by the OS are used. (NumCores=0)}

\item{Xtrain}{an n by d numeric matrix (preferable) or data frame. This should be the same data matrix/frame used to train the forest, and is only required if RerF was called with rank.transform = TRUE. (Xtrain=NULL)}

\item{aggregate.output}{if TRUE then the tree predictions are aggregated weighted by their probability estimates. Otherwise, the individual tree probabilities are returned. (aggregate.output=TRUE)

TODO: Remove option for aggregate output? Only options for returning aggregate predictions or probabilities}

\item{output.scores}{if TRUE then predicted class scores (probabilities) for each observation are returned rather than class labels. (output.scores = FALSE)}
}
\value{
predictions an n length vector of predictions
}
\description{
Predicts the classification of samples using a trained forest.
}
\examples{
library(rerf)
trainIdx <- c(1:40, 51:90, 101:140)
X <- as.matrix(iris[, 1:4])
Y <- as.numeric(iris[, 5])
forest <- RerF(X[trainIdx, ], Y[trainIdx], num.cores = 1L, rank.transform = TRUE)
# Using a set of samples with unknown classification
predictions <- Predict(X[-trainIdx, ], forest, num.cores = 1L, Xtrain = X[trainIdx, ])
error.rate <- mean(predictions != Y[-trainIdx])
}