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As a business rules developer, you can use Predictive Model Markup Language (PMML) to define statistical or data-mining models that you can integrate with your decision services in Red Hat Decision Manager. Red Hat Decision Manager includes consumer conformance support of PMML 4.2.1 for Regression, Scorecard, Tree, and Mining models.
Predictive Model Markup Language (PMML) has been around a long time and well known and used by certain groups of data scientists who have been around a while. It is also widely supported by many analytics vendors, and provides an inter-change format to allow predictive models to be described and exchanged.
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The models created by data mining tools can be ported to production applications by utilizing the Predictive Model Markup Language (PMML) Guazzelli et al., 2009) or by invoking data mining tools in the production application. PMML provides a portable and consistent format of model description which can be read by most Predictive Analytics and.
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PMML (Predictive Model Markup Language) When serializing and deserializing curated machine learning models for deployment, a widely used format is an XML-based PMML developed at the NCDM (National Center for Data Mining) University of Illinois. While mature, PMML still lacks broad adoption in the industry across data science languages and tools.
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PMML gives an approach to analyze the application for description and discussion about the predictive model prepared by data mining and machine learning algorithms [7. It is based on XML and capabilities for logistic regression, and the feedforward neural network advance version supports NB, KNN, and SVM classifiers.
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