pmml.nnet {pmml} | R Documentation |
Generate the PMML representation for a nnet object (Neural Network). The nnet object is converted into a PMML representation. The PMML can then be imported into other systems that accept PMML. With this code, a PMML representation can be obtained for Neural Networks implementing classification (multi-class and binary) as well as regression.
## S3 method for class 'nnet': pmml(model, model.name="NeuralNet_model", app.name="Rattle/PMML", description="Neural Network PMML Model", copyright=NULL, ...)
model |
a nnet object. |
model.name |
a name to give to the model in the PMML. |
app.name |
the name of the application that generated the PMML. |
description |
a descriptive text for the header of the PMML. |
copyright |
the copyright notice for the model. |
... |
further arguments passed to or from other methods. |
The generated PMML can be imported into any PMML consuming application that recognizes PMML 3.2. An example is ADAPA. ADAPA (Adaptive Decision and Predictive Analytics) is a lightweight decision engine that offers at its core batch and real-time scoring of predictive models as well as fast execution of business rules. ADAPA supports an extensive collection of PMML elements, including the following predictive techniques: 1) Neural Networks (Backprogagation and Neural Gas); 2) Support Vector Machines; 3) Linear and Logistic Regression as well as all general regression PMML models: a) Multinomial Logistic; b) General Linear; 3) Ordinal Multinomial; 4) Simple Regression; and 5) Generalized Linear Model. ADAPA provides a reliable and fast way to manage, deploy, and execute a multitude of models and decision strategies.
Package home page: http://rattle.togaware.com
PMML home page: http://www.dmg.org
Zementis' useful PMML convert: http://www.zementis.com/pmml_converters.htm
ADAPA home page: http://www.zementis.com/adapa.htm
pmml
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