Transforms Input Data from a PMML Perspective
Add a function transformation to a WrapData object.
Data Transformations for PMML output
Discretizes a continuous variable to a discrete one as indicated by in...
Initialize internal variables in a WrapData object
Implements a map between discrete values in accordance to the PMML ele...
Normalizes continuous values in accordance to the PMML element: NormCo...
Normalize discrete values in accordance to the PMML element: NormDiscr...
Renames a variable in the WrapData transform object
Wrap raw data in an R object
Performs a z-score normalization on continuous values in accordance to...
Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.