pmml.ada: Generate the PMML representation for an ada object from the...

In pmml: Generate PMML for Various Models

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

Description

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 = "SoftwareAG PMML Generator",
  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).

Author(s)

Wen Lin

References

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

Examples

## Not run: 
library(ada)
data(audit)

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

## End(Not run)

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