Since PMML is an XML-based standard, the specification comes in the form of an XML schema. PMML itself is a mature standard with over 30 organizations having announced products supporting PMML.[3]
PMML components
A PMML file can be described by the following components:[4][5]
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 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 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 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: allows 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. The latest release of PMML, PMML 4.1, 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 only for pre-processing became available for post-processing too.
Version 4.4 was released in November 2019.[15][16]
Release history
Version
Release date
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
Version 4.1
December 2011
Version 4.2
February 2014
Version 4.2.1
March 2015
Version 4.3
August 2016
Version 4.4
November 2019
Data Mining Group
The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008.[17] The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML.
↑"Data Mining Group". Retrieved December 14, 2017. The DMG is proud to host the working groups that develop the Predictive Model Markup Language (PMML) and the Portable Format for Analytics (PFA), two complementary standards that simplify the deployment of analytic models.
↑"PMML Powered". Data Mining Group. Retrieved December 14, 2017.
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