pmml.randomForest {pmml} | R Documentation |
Generate the PMML representation for a randomForest object from the package randomForest.
Description
Generate the PMML representation for a randomForest object from the package randomForest.
Usage
## S3 method for class 'randomForest'
pmml(
model,
model_name = "randomForest_Model",
app_name = "SoftwareAG PMML Generator",
description = "Random Forest Tree Model",
copyright = NULL,
model_version = NULL,
transforms = NULL,
missing_value_replacement = NULL,
parent_invalid_value_treatment = "returnInvalid",
child_invalid_value_treatment = "asIs",
...
)
Arguments
model |
A randomForest 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. |
parent_invalid_value_treatment |
Invalid value treatment at the top MiningField level. |
child_invalid_value_treatment |
Invalid value treatment at the model segment MiningField level. |
... |
Further arguments passed to or from other methods. |
Details
This function outputs a Random Forest in PMML format.
Value
PMML representation of the randomForest object.
Author(s)
Tridivesh Jena
References
randomForest: Breiman and Cutler's random forests for classification and regression
Examples
## Not run:
# Build a randomForest model
library(randomForest)
iris_rf <- randomForest(Species ~ ., data = iris, ntree = 20)
# Convert to pmml
iris_rf_pmml <- pmml(iris_rf)
rm(iris_rf)
## End(Not run)