Random forest regression — wbt_random_forest

Performs a random forest regression analysis using training site data and predictor rasters.

Usage

wbt_random_forest_regression(
  inputs,
  training,
  field,
  output = NULL,
  n_trees = 100,
  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 points shapefile.
field: Name of the attribute containing response variable name data.
output: Name of the output raster file. This parameter is optional. When unspecified, the tool will only build the model. When specified, the tool will use the built model and predictor rasters to perform a spatial prediction.
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.