pmml.gbm: Generate the PMML representation for a gbm object from the...

In pmml: Generate PMML for Various Models

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

Description

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

Usage

## S3 method for class '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.

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".

Value

PMML representation of the gbm object.

Author(s)

Tridivesh Jena

References

gbm: Generalized Boosted Regression Models (on CRAN)

Examples

## Not run: 
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)

## End(Not run)

pmml documentation built on March 18, 2022, 5:49 p.m.