wbt_random_forest_classification: Random forest classification
In whitebox: 'WhiteboxTools' R Frontend

wbt_random_forest_classification

R Documentation

Random forest classification

Description

Performs a supervised random forest classification using training site polygons/points and predictor rasters.

Usage

wbt_random_forest_classification(
  inputs,
  training,
  field,
  output = NULL,
  split_criterion = "Gini",
  n_trees = 500,
  min_samples_leaf = 1,
  min_samples_split = 2,
  test_proportion = 0.2,
  wd = NULL,
  verbose_mode = NULL,
  compress_rasters = NULL,
  command_only = FALSE
)

Arguments

`inputs`	Names of the input predictor rasters.
`training`	Name of the input training site polygons/points shapefile.
`field`	Name of the attribute containing class data.
`output`	Name of the output raster file.
`split_criterion`	Split criterion to use when building a tree. Options include 'Gini', 'Entropy', and 'ClassificationError'.
`n_trees`	The number of trees in the forest.
`min_samples_leaf`	The minimum number of samples required to be at a leaf node.
`min_samples_split`	The minimum number of samples required to split an internal node.
`test_proportion`	The proportion of the dataset to include in the test split; default is 0.2.
`wd`	Changes the working directory. Default: `NULL` will use the value in WhiteboxTools settings, see `wbt_wd()` for details.
`verbose_mode`	Sets verbose mode. If verbose mode is `FALSE`, tools will not print output messages. Default: `NULL` will use the value in WhiteboxTools settings, see `wbt_verbose()` for details.
`compress_rasters`	Sets the flag used by 'WhiteboxTools' to determine whether to use compression for output rasters. Default: `NULL` will use the value in WhiteboxTools settings, see `wbt_compress_rasters()` for details.
`command_only`	Return command that would be executed by `system()` rather than running tool. Default: `FALSE`.