StratifiedRF (version 0.2.2)

predict.stratified_rf: Make predictions on new data

Description

Make predictions from a stratified_rf model on new data.

Usage

# S3 method for stratified_rf
predict(object, data, type = "class",
  agg_type = "prob", vote_type = "simple", na.action = na.pass,
  threshold = NULL, ...)

Arguments

object

A stratified_rf model.

data

New data on which to make predictions (data.frame only). Must have the same names as the data used to build the model.

type

Prediction type. Either "class" to get the predicted class or "prob" to get the voting scores for each class.

agg_type

How to combine the predictions from individual trees. Either "prob" to average the probabilities output from each tree or "class" to count the final predictions from each.

vote_type

How to weight the outputs from each tree. Either "simple" to average them, or "weighted" for a weighted average according to their OOB classification accuracy.

na.action

Function indicating how to handle missing values (see the 'C50' documentation for details).

threshold

Count only votes from trees whose out-of-bag classification accuracy is above this threshold. Must be a number between 0 and 1.

...

other options (not currently used)

Details

Note that by default, for classification models the predictions are made quite differently from the original Random Forest algorithm.

See Also

'C50' library: https://cran.r-project.org/package=C50

Examples

data(iris)
groups <- list(c("Sepal.Length","Sepal.Width"),c("Petal.Length","Petal.Width"))
mtry <- c(1,1)
m <- stratified_rf(iris,"Species",groups,mtry,ntrees=2,multicore=FALSE)
predict(m,iris)