OOBPredict: Compute out-of-bag predictions

In rerf: Randomer Forest

Description

Computes out-of-bag class predictions for a forest trained with store.oob=TRUE.

Usage

OOBPredict(X, forest, num.cores = 0L, Xtrain = NULL,
  output.scores = FALSE)

Arguments

X	an n sample by d feature matrix (preferable) or data frame which was used to train the provided forest.
forest	a forest trained using the RerF function, with store.oob=TRUE.
num.cores	the number of cores to use while training. If num.cores=0 then 1 less than the number of cores reported by the OS are used. (num.cores=0)
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)
output.scores	if TRUE then predicted class scores (probabilities) for each observation are returned rather than class labels. (output.scores = FALSE)

Value

predictions a length n vector of predictions in a format similar to the Y vector used to train the forest

Examples

library(rerf)
X <- as.matrix(iris[, 1:4])
Y <- iris[[5L]]
forest <- RerF(X, Y, store.oob = TRUE, num.cores = 1L)
predictions <- OOBPredict(X, forest, num.cores = 1L)
oob.error <- mean(predictions != Y)

rerf documentation built on May 2, 2019, 8:16 a.m.