Predictive Model Markup Language

File:PMML Logo.png

The Predictive Model Markup Language (PMML) is an XML-based markup language developed by the Data Mining Group (DMG) to provide a way for applications to define models related to predictive analytics and data mining and to share those models between PMML-compliant applications.

PMML provides applications a vendor-independent method of defining models so that proprietary issues and incompatibilities are no longer a barrier to the exchange of models between applications. It allows users to develop models within one vendor's application and use other vendors' applications to visualize, analyze, evaluate or otherwise use the models. Previously, this was very difficult, but with PMML, the exchange of models between compliant applications is straightforward.

Since PMML is an XML-based standard, the specification comes in the form of an XML schema.

PMML Components

PMML follows an intuitive structure to describe a data mining model, be it an artificial neural network or a logistic regression model.

File:PMMLComponents.jpg

Sequentially, it 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.
  • Normalization: map values to numbers, the input can be continuous or discrete.
  • Discretization: map continuous values to discrete values.
  • Value mapping: map discrete values to discrete values.
  • Functions: derive a value by applying a function to one or more parameters.
  • Aggregation: used to summarize or collect groups of values.

• Model: contains the definition of the data mining model. A multi-layered feedforward neural network is the most common neural network representation in contemporary applications, given the popularity and efficacy associated with its training algorithm known as backpropagation. Such a network is represented in PMML by a "NeuralNetwork" element which contains attributes such as:
  • Model Name (attribute modelName)
  • Function Name (attribute functionName)
  • Algorithm Name (attribute algorithmName)
  • Activation Function (attribute activationFunction)
  • Number of Layers (attribute numberOfLayers)

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 data mining models including support vector machines, association rules, Naive Bayes classifier, clustering models, text models, decision trees, and different regression models.

• Mining Schema: the mining schema lists all fields used in the model. This can be a subset of the fields as defined in the data dictionary. It contains specific information about each field, such as:
  • Name (attribute name): must refer to a field in the data dictionary
  • Usage type (attribute usageType): defines the way a field is to be used in the model. Typical values are: active, predicted, and supplementary. Predicted fields are those whose values are predicted by the model.
  • Outlier Treatment (attribute outliers): defines the outlier treatment to be use. In PMML, outliers can be treated as missing values, as extreme values (based on the definition of high and low values for a particular field), or as is.
  • Missing Value Replacement Policy (attribute missingValueReplacement): if this attribute is specified then a missing value is automatically replaced by the given values.
  • Missing Value Treatment (attribute missingValueTreatment): indicates how the missing value replacement was derived (e.g. as value, mean or median).

• Targets: allow for post-processing of the predicted value in the format of scaling if the output of the model is continuous. Targets can also be used for classification tasks. In this case, the attribute priorProbability specifies a default probability for the corresponding target category. It is used if the prediction logic itself did not produce a result. This can happen, e.g., if an input value is missing and there is no other method for treating missing values.

• Output: this element can be used to name all the desired output fields expected from the model. These are features of the predicted field and so are typically the predicted value itself, the probability, cluster affinity (for clustering models), standard error, etc.

PMML 4.0

The latest version of PMML, 4.0, was released on June 16, 2009.

Examples of new features include:

• Improved Pre-Processing Capabilities: Additions to built-in functions include a range of Boolean operations and an If-Then-Else function.

• Time Series Models: New exponential Smoothing models; also place holders for ARIMA, Seasonal Trend Decomposition, and Spectral Analysis, which are to be supported in the near future.

• Model Explanation: Saving of evaluation and model performance measures to the PMML file itself.

• Multiple Models: Capabilities for model composition, ensembles, and segmentation (e.g., combining of regression and decision trees).

• Extensions of Existing Elements: Addition of multi-class classification for Support Vector Machines, improved representation for Association Rules, and the addition of Cox Regression Models.

Release history

Version 0.7	July 1997
Version 0.9	July 1998
Version 1.0	August 1999
Version 1.1	August 2000
Version 2.0	August 2001
Version 2.1	March 2003
Version 3.0	October 2004
Version 3.1	December 2005
Version 3.2	May 2007
Version 4.0	June 2009

PMML Products

A range of products are being offered to produce and consume PMML:

• IBM DB2 Data Warehouse Edition: produces PMML 3.0 and 3.1 for sequences only models. Consumes (scores and visualizes) PMML 3.1 and earlier.

• KNIME: produces and consumes PMML 3.1 for neural networks, decision trees, clustering models, regression models, and support vector machines.

• KXEN: produces PMML 3.2 for regression models and clustering.

• Microsoft SQL Server 2008 Analysis Server: produces and consumes PMML 2.1 for decision trees and clustering.

• MicroStrategy: supports PMML 2.0, 2.1, 3.0, 3.1, 3.2 and 4.0 for linear regression, logistic regression, decision trees, clustering, association rules, time series, neural networks and support vector machines.

• Rattle/R: Uses the R programming language to build several predictive models. It offers a PMML package to export models built in R to PMML 3.2. This package includes export support for support vector machines, linear regression, logistic regression, decision trees, random forests, random survival forests, neural networks, K-means and hierarchical clustering, and association rules.
• Salford-Systems CART: a decision tree system that produces PMML 3.1.

• SAS Enterprise Miner: produces PMML 2.1 and 3.1 for several mining models, including linear regression, logistic regression, decision trees, neural networks, K-means clustering, and association rules.

• SPSS: produces and scores PMML 3.2 for a variety of models.

• STATISTICA: generates PMML 2.0 and 3.0 for analyses such as linear regression, logistic regression, decision trees, support vector machines, and neural networks

• Zementis ADAPA: batch and real-time scoring of PMML 3.2 and earlier for several mining models, including decision trees, support vector machines, neural networks, naive bayes, linear and logistic regression models as well as clustering models.

External links

• Data Mining Group Home
• Data Pre-processing in PMML and ADAPA - A Primer
• Information on how to use the PMML Converter
• PMML 3.2 Specification
• PMML 4.0 Specification
• PMML Converter - This is an iGoogle gadget that can be used to convert a variety of PMML elements from older versions (2.1, 3.0 and 3.1) to PMML 3.2. It also validates any PMML file against the PMML schema (for older versions as well as version 3.2) and corrects known issues with the exports from some vendors.
• PMML Discussion Group - Analytic Bridge
• PMML Interest Group - LinkedIn
• PMML Knol (sister page)