pmml (version 2.5.2)

pmml.naiveBayes: Generate the PMML representation for a naiveBayes object from the package e1071.

Description

Generate the PMML representation for a naiveBayes object from the package e1071.

Usage

# S3 method for naiveBayes
pmml(
  model,
  model_name = "naiveBayes_Model",
  app_name = "SoftwareAG PMML Generator",
  description = "NaiveBayes Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  predicted_field,
  ...
)

Arguments

model

A naiveBayes 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.

predicted_field

Required parameter; the name of the predicted field.

...

Further arguments passed to or from other methods.

Value

PMML representation of the naiveBayes object.

Details

The PMML representation of the NaiveBayes model implements the definition as specified by the Data Mining Group: intermediate probability values which are less than the threshold value are replaced by the threshold value. This is different from the prediction function of the e1071 in which only probability values of 0 and standard deviations of continuous variables of with the value 0 are replaced by the threshold value. The two values will therefore not match exactly for cases involving very small probabilities.

References

• e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien (on CRAN)

• A. Guazzelli, T. Jena, W. Lin, M. Zeller (2013). Extending the Naive Bayes Model Element in PMML: Adding Support for Continuous Input Variables. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Examples

# NOT RUN {
library(e1071)

data(houseVotes84)
house <- na.omit(houseVotes84)

model <- naiveBayes(Class ~ V1 + V2 + V3, data = house, threshold = 0.003)

model_pmml <- pmml(model, dataset = house, predicted_field = "Class")
# }