View source: R/machine_learning.R
wbt_svm_regression | R Documentation |
Performs a supervised SVM regression analysis using training site points and predictor rasters.
wbt_svm_regression(
inputs,
training,
field,
scaling = "Normalize",
output = NULL,
c = 50,
eps = 10,
gamma = 0.5,
test_proportion = 0.2,
wd = NULL,
verbose_mode = NULL,
compress_rasters = NULL,
command_only = FALSE
)
inputs |
Names of the input predictor rasters. |
training |
Name of the input training site points Shapefile. |
field |
Name of the attribute containing class data. |
scaling |
Scaling method for predictors. Options include 'None', 'Normalize', and 'Standardize'. |
output |
Name of the output raster file. |
c |
c-value, the regularization parameter. |
eps |
Epsilon in the epsilon-SVR model. |
gamma |
Gamma parameter used in setting the RBF (Gaussian) kernel function. |
test_proportion |
The proportion of the dataset to include in the test split; default is 0.2. |
wd |
Changes the working directory. Default: |
verbose_mode |
Sets verbose mode. If verbose mode is |
compress_rasters |
Sets the flag used by 'WhiteboxTools' to determine whether to use compression for output rasters. Default: |
command_only |
Return command that would be executed by |
Returns the tool text outputs.
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