The Predictive Model Markup Language (PMML) is an

XML Extensible Markup Language (XML) is a markup language and file format for storing, transmitting, and reconstructing data. It defines a set of rules for encoding electronic document, documents in a format that is both human-readable and Machine-r ...

-based predictive model interchange format conceived by Robert Lee Grossman, then the director of the National Center for Data Mining at the

University of Illinois at Chicago The University of Illinois Chicago (UIC) is a public research university in Chicago, Illinois, United States. Its campus is in the Near West Side community area, adjacent to the Chicago Loop. The second campus established under the Universi ...

. PMML provides a way for analytic applications to describe and exchange predictive models produced by

data mining Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and ...

and

machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...

algorithms. It supports common models such as

logistic regression In statistics, a logistic model (or logit model) is a statistical model that models the logit, log-odds of an event as a linear function (calculus), linear combination of one or more independent variables. In regression analysis, logistic regres ...

and other

feedforward neural network Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights to obtain outputs (inputs-to-output): feedforward. Recurrent neural networks, or neur ...

s. Version 0.9 was published in 1998. Subsequent versions have been developed by the Data Mining Group. Since PMML is an XML-based standard, the specification comes in the form of an

XML schema An XML schema is a description of a type of XML document, typically expressed in terms of constraints on the structure and content of documents of that type, above and beyond the basic syntactical constraints imposed by XML itself. These constrai ...

. PMML itself is a mature standard with over 30 organizations having announced products supporting PMML.

PMML components

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 A data dictionary, or metadata repository, as defined in the ''IBM Dictionary of Computing'', is a "centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format". ''Oracle Corporation, ...

: 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 A model is an informative representation of an object, person, or system. The term originally denoted the plans of a building in late 16th-century English, and derived via French and Italian ultimately from Latin , . Models can be divided in ...

: contains the definition of the data mining model. E.g., A multi-layered

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 In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laborato ...

, association rules,

Naive Bayes classifier In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of " probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. In other words, a naive Bayes model assumes th ...

, clustering models, text models,

decision trees A decision tree is a decision support system, decision support recursive partitioning structure that uses a Tree (graph theory), tree-like Causal model, model of decisions and their possible consequences, including probability, chance event ou ...

, and different

regression models Regression or regressions may refer to: Arts and entertainment * ''Regression'' (film), a 2015 horror film by Alejandro Amenábar, starring Ethan Hawke and Emma Watson * ''Regression'' (magazine), an Australian punk rock fanzine (1982–1984) * ...

. * 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.

PMML 4.0, 4.1, 4.2 and 4.3

PMML 4.0 was released on June 16, 2009. 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 In computer science, conditionals (that is, conditional statements, conditional expressions and conditional constructs) are programming language constructs that perform different computations or actions or return different values depending on t ...

function. *

Time Series In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. ...

Models: New exponential

Smoothing In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the d ...

models; also place holders for

ARIMA Arima, officially The Royal Chartered Borough of Arima is the easternmost and second largest in area of the three boroughs of Trinidad and Tobago. It is geographically adjacent to Sangre Grande and Arouca at the south central foothills of the ...

, 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 In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary ...

for

Support Vector Machines In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laborato ...

, improved representation for Association Rules, and the addition of Cox Regression Models. PMML 4.1 was released on December 31, 2011. New features included: * 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). PMML 4.2 was released on February 28, 2014. New features include: * Transformations: New elements for implementing text mining * New built-in functions for implementing regular expressions: matches, concat, and replace * Simplified outputs for post-processing * Enhancements to Scorecard and Naive Bayes model elements PMML 4.3 was released on August 23, 2016. New features include: * New Model Types: ** Gaussian Process ** Bayesian Network * New built-in functions * Usage clarifications * Documentation improvements Version 4.4 was released in November 2019.

Release history

Data Mining Group

Th
Data Mining Group
is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML.

References

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External links