Generate the PMML representation for an ada object from the package ada.

Usage

# S3 method for class 'ada'
pmml(
  model,
  model_name = "AdaBoost_Model",
  app_name = "R PMML Generator - Package pmml",
  description = "AdaBoost Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  ...
)

Arguments

model: An ada 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

Export the ada model in the PMML MiningModel (multiple models) format. The MiningModel element consists of a list of TreeModel elements, one in each model segment.

This function implements the discrete adaboost algorithm only. Note that each segment tree is a classification model, returning either -1 or 1. However the MiningModel (ada algorithm) is doing a weighted sum of the returned value, -1 or 1. So the value of attribute functionName of element MiningModel is set to "regression"; the value of attribute functionName of each segment tree is also set to "regression" (they have to be the same as the parent MiningModel per PMML schema). Although each segment/tree is being named a "regression" tree, the actual returned score can only be -1 or 1, which practically turns each segment into a classification tree.

The model in PMML format has 5 different outputs. The "rawValue" output is the value of the model expressed as a tree model. The boosted tree model uses a transformation of this value, this is the "boostValue" output. The last 3 outputs are the predicted class and the probabilities of each of the 2 classes (The ada package Boosted Tree models can only handle binary classification models).

References

ada: an R package for stochastic boosting (on CRAN)

Author

Wen Lin

Examples

if (FALSE) { # \dontrun{
library(ada)
data(audit)

fit <- ada(Adjusted ~ Employment + Education + Hours + Income, iter = 3, audit)
fit_pmml <- pmml(fit)
} # }