Introduction
This vignette lists the packages and model types supported by pmml, as well as functions that extend the basic exporter functionality.
Supported packages
The following packages and model types are supported by pmml.
- Clustering
- K Nearest Neighbors
- Linear Models
- glmnet:
cv.glmnet with “gaussian” and “poisson” family types
- nnet:
multinom
- stats:
glm, lm without interaction terms
- Naive Bayes
- Neural Networks
- Support Vector Machines
- e1071:
svm
- kernlab:
ksvm with “rbfdot”, “polydot”, “vanilladot”, and “tanhdot” kernels.
- Tree-based Models and Ensembles
- ada:
ada
- gbm:
gbm with “bernoulli”, “poisson”, and “multinomial” distribution types
- randomForest:
randomForest
- randomForestSRC (version 2.5.0):
randomSurvivalForest
- rpart:
rpart
- xgboost:
xgb.Booster with “multi:softprob”, “multi:softmax”, and “binary:logistic” objectives
- Other Packages
Additional functions
The following functions in pmml allow additional operations, such as creating specific nodes and adding attributes. For more information, check the documentation for each function.
- AddAttributes: add attribute values to an existing element in a given PMML file.
- AddDDAttributes: add attribute values to an existing DataField element in a given PMML file.
- addDFChildren: add 'Interval' and 'Value' child elements to a given DataField element in a given PMML file.
- addLT: add a LocalTransformations element to a given PMML file.
- addMSAttributes: add attribute values to an existing MiningField element in a given PMML file.
- addOutputField: add Output nodes to a PMML object.
- fileToXMLNode: read in a file and parse it into an object of type XMLNode.
- functionToPMML: convert an R expression to PMML.
- makeIntervals: create Interval elements to add to a DataDictionary element.
- makeOutputNodes: add Output nodes to a PMML object.
- makeValues: create Values element to add to a DataDictionary element.
References