predict.stratified_rf: Make predictions on new data

In david-cortes/StratifiedRF: Builds Trees by Sampling Variables in Groups

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.

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)

david-cortes/StratifiedRF documentation built on May 24, 2019, 7:25 p.m.