Random forest classification — wbt_random_forest

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.

Value

Returns the tool text outputs.