Generate the PMML representation for a gbm object from the package gbm.

# S3 method for gbm
pmml(
  model,
  model_name = "GBM_Model",
  app_name = "SoftwareAG PMML Generator",
  description = "Generalized Boosted Tree Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  ...
)

Arguments

model: A gbm object.
model_name: A name to be given to the PMML model.
app_name: The name of the application that generated the PMML.
description: A descriptive text for the Header element of the PMML.
copyright: The copyright notice for the model.
model_version: A string specifying the model version.
transforms: Data transformations.
missing_value_replacement: Value to be used as the 'missingValueReplacement' attribute for all MiningFields.
...: Further arguments passed to or from other methods.

Value

PMML representation of the gbm object.

Details

The 'gbm' function uses various distribution types to fit a model; currently only the "bernoulli", "poisson" and "multinomial" distribution types are supported.

For all cases, the model output includes the gbm prediction type "link" and "response".

References

gbm: Generalized Boosted Regression Models (on CRAN)

Author

Tridivesh Jena

Examples

if (FALSE) {
library(gbm)
data(audit)

mod <- gbm(Adjusted ~ .,
  data = audit[, -c(1, 4, 6, 9, 10, 11, 12)],
  n.trees = 3, interaction.depth = 4
)

mod_pmml <- pmml(mod)

# Classification example:
mod2 <- gbm(Species ~ .,
  data = iris, n.trees = 2,
  interaction.depth = 3, distribution = "multinomial"
)

# The PMML will include a regression model to read the gbm object outputs
# and convert to a "response" prediction type.
mod2_pmml <- pmml(mod2)
}