R: Make predictions on new data

predict.stratified_rf {StratifiedRF}

R Documentation

Make predictions on new data

Description

Make predictions from a stratified_rf model on new data.

Usage

## S3 method for class '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.

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)