predict.multifor: Random forest prediction using a saved forest from 'multifor'

In diversityForest: Innovative Complex Split Procedures in Random Forests Through Candidate Split Sampling

Random forest prediction using a saved forest from multifor

Description

Prediction with new data and a saved forest from multifor.

Usage

## S3 method for class 'multifor'
predict(
  object,
  data = NULL,
  predict.all = FALSE,
  num.trees = object$num.trees,
  type = "response",
  seed = NULL,
  num.threads = NULL,
  verbose = TRUE,
  ...
)

Arguments

object	multifor object.
data	New test data of class data.frame.
predict.all	Return individual predictions for each tree instead of aggregated predictions for all trees. Return a matrix (sample x tree) for classification, a 3d array for probability estimation (sample x class x tree).
num.trees	Number of trees used for prediction. The first num.trees in the forest are used.
type	Type of prediction. If "response" (default), the predicted classes (classification) or predicted probabilities (probability estimation) are returned. If "terminalNodes", the IDs of the terminal node in each tree for each observation in the given dataset are returned.
seed	Random seed. Default is NULL, which generates the seed from R. Set to 0 to ignore the R seed. The seed is used in case of ties in classification mode.
num.threads	Number of threads. Default is number of CPUs available.
verbose	Verbose output on or off.
...	further arguments passed to or from other methods.

Details

This package is a fork of the R package 'ranger' that implements random forests using an efficient C++ implementation. More precisely, 'diversityForest' was written by modifying the code of 'ranger', version 0.11.0.

Value

Object of class multifor.prediction with elements

predictions	Predicted classes/values (only for classification and regression)
num.trees	Number of trees.
num.independent.variables	Number of independent variables.
num.samples	Number of samples.
treetype	Type of forest/tree. Classification or probability.

Author(s)

Marvin N. Wright

References

• Hornung, R. (2022). Diversity forests: Using split sampling to enable innovative complex split procedures in random forests. SN Computer Science 3(2):1, <\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s42979-021-00920-1")}>.

• Wright, M. N., Ziegler, A. (2017). ranger: A fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software 77:1-17, <\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v077.i01")}>.

• Wager, S., Hastie T., & Efron, B. (2014). Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife. Journal of Machine Learning Research 15:1625-1651.

• Meinshausen (2006). Quantile Regression Forests. Journal of Machine Learning Research 7:983-999.

See Also

multifor

diversityForest documentation built on June 8, 2025, 1:23 p.m.