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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.

Sequentially, it can be described by the following components:^[1][2]

• 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 (custom and built-in): 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. E.g., A multi-layered feedforward neural network is the most common neural network representation in contemporary applications. 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 4.1, the latest release of PMML, extended output to allow for generic post-processing of model outputs. In PMML 4.1, all the built-in and custom functions that were originally available for pre-processing only are now also available for post-processing.

PMML 4.0 and 4.1

The previous version of PMML, 4.0, was released on June 16, 2009.^[3][4][5]

Examples of new features included:

• 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 density estimation, 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.

The latest version of PMML, 4.1, was released on December 31, 2011.^[6][7]

New features include:

• New model elements for representing Scorecards, k-Nearest Neighbors (KNN) and Baseline Models.

• Simplification of multiple models. In PMML 4.1, the same element is used to represent model segmentation, ensemble, and chaining.

• Overall definition of field scope and field names.

• A new attribute that identifies for each model element if the model is ready or not for production deployment.

• Enhanced post-processing capabilities (via the Output element).

Release history

References

External links

• Data Mining Group Home
• Data Pre-processing in PMML and ADAPA - A Primer
• Information on how to use the PMML Converter
• Video of Dr. Alex Guazzelli's PMML presentation for the ACM Data Mining Group (hosted by LinkedIn)
• PMML 3.2 Specification
• PMML 4.0 Specification
• PMML 4.1 Specification
• PMML Interest Group - LinkedIn
• What is PMML? Explore the power of predictive analytics and open standards - Article published on the IBM developerWorks website.
• Representing predictive solutions in PMML: Move from raw data to predictions - Article published on the IBM developerWorks website.
• Predictive analytics in healthcare: The importance of open standards - Article published on the IBM developerWorks website.