Predictive Model Markup Language PMML)

Erin Velez

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PMML stands for "Predictive Model Markup Language. It is the de facto standard to represent predictive solutions. It is the de facto standard to represent predictive solutions. A PMML file may contain a myriad of data transformations (pre- and post-processing) as well as one or more predictive models. PMML (Predictive Model Markup Language) is an XML-based language that enables the definition and sharing of predictive models between applications. A predictive model is a statistical model that is designed to predict the likelihood of target occurrences given established variable s or factors.

Aug 14, 2019 PMML (Predictive Model Markup Language) provides a standard way to represent data mining models so that these can be shared between different statistical applications. Predictive Model Markup Language (PMML) download. What is PMML. IBM. Predictive Model Markup Language (PMML) is an XML-based markup language designed to provide a method of defining application models related to predictive analytics and data mining. PMML attempts to eliminate proprietary issues and incompatibility from application exchange models. Predictive Model Markup Language (PMML. Git tools.

Predictive Model Markup Language. The Predictive Model Markup Language (PMML) is the de facto standard language used to represent predictive analytic models. It allows for predictive solutions to be easily shared between PMML compliant applications. With predictive analytics, the Petroleum and Chemical industries create solutions to predict machinery break-down and ensure safety. Big Data Business Intelligence Predictive Analytics Reporting. Collaboration. Collaboration. Team Collaboration Idea Management Conferencing CAD. Communications. Communications. Call Center Call Recording Call Tracking IVR Predictive Dialer Telephony VoIP Web Conferencing. Predictive Model Markup Language (PMML.

What is PMML. KDnuggets.
What is PMML (Predictive Model Markup Language.
Predictive Model Markup Language (PMML) download.
Predictive Model Markup Language (PMML) is supported by leading business intelligence and analytics vendors like IBM, SAS, MicroStrategy, Oracle and SAP. The XML -based PMML enables sharing predictive analytic models between different applications, making it possible, for example, to build a model in one system and then move it to another system to test its performance against a test data set.

A PMML file can be described by the following components: • Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also contains an attribute for a timestamp which can be used to specify the dat.A PMML file can be described by the following components: • Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also contains an attribute for a timestamp which can be used to specify the date of model creation.• Data Dictionary: contains definitions for all the possible fields used by the model. It is here that a field is defined as continuous, categorical, or ordinal (attribute optype. Depending on this definition, the appropriate value ranges are then defined as well as the data type (such as, string or double. • Data Transformations: transformations allow for the mapping of user data into a more desirable form to be used by the mining model. PMML defines several kinds of simple data transformations. • Model: contains the definition of the data mining model. E.g., A multi-layered feedforward neural network is represented in PMML by a "NeuralNetwork" element which contains attributes such as: This information is then followed by three kinds of neural layers which specify the architecture of the neural network model being represented in the PMML document. These attributes are NeuralInputs, NeuralLayer, and NeuralOutputs. Besides neural networks, PMML allows for the representation of many other types of models including support vector machines, association rules, Naive Bayes classifier, clustering models, text models, decision trees, and different regression models.• Mining Schema: a list of all fields used in the model. This can be a subset of the fields as defined in more on Wikipedia.