PMML 4.0

There are some nice changes in the PMML 4.0 version. PMML is the XML version for data modeling , or specificallyquoting the DMG group itself

PMML uses XML to represent mining models. The structure of the models is described by an XML Schema. One or more mining models can be contained in a PMML document. A PMML document is an XML document with a root element of type PMML. The general structure of a PMML document is:
  <?xml version="1.0"?>
  <PMML version="4.0"
    xmlns="http://www.dmg.org/PMML-4_0"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" >

    <Header copyright="Example.com"/>
    <DataDictionary> ... </DataDictionary>

    ... a model ...

  </PMML>

So what is new in version 4. Here are some powerful modeling changes. For anyone with any XML knowledge PMML is the way to go.

PMML 4.0 – Changes from PMML 3.2

Associations

Itemset and AssociationRule elements are no longer enclosed within a “Choice” element

Added different scoring procedures: recommendation, exclusiveRecommendation and ruleAssociation with explanation and example

Changed version to “4.0” from “3.2” in the example(s)

BuiltinFunctions

Added the following functions:

isMissing

isNotMissing

equal

notEqual

lessThan

lessOrEqual

greaterThan

greaterOrEqual

isIn

isNotIn

and

or

not

isIn

isNotIn

if

Click on Image for better resolution

ClusteringModel

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

Conformance

Changed all version references from “3.2” to “4.0”

DataDictionary

No changes

Functions

No changes

GeneralRegression

Changed to allow for Cox survival models and model ensembles

Add new model type: CoxRegression.

Allow empty regression model when model type is CoxRegression, so that baseline-only model could be represented.

Add new optional model attributes: endTimeVariable, startTimeVariable, subjectIDVariable, statusVariable, baselineStrataVariable, modelDF.

Add optional Matrix in Predictor to specify a contrast matrix, optional attribute referencePoint in Parameter.

Add new elements: BaseCumHazardTables, EventValues, BaselineStratum, BaselineCell.

Add examples of scoring for Cox Regression and contrast matrices.

Add new type of distribution: tweedie.

Add new attribute in model: targetReferenceCategory, so that the model can be used in MiningModel.

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

GeneralStructure

Updated the XML namespace to “http://www.dmg.org/PMML-4_0” from “http://www.dmg.org/PMML-3_2”

Added TimeSeriesModel to the PMML XSD

Header

No changes

Interoperability

Changed: “As a result, a new approach for interoperability was required and is being introduced in PMML version 3.2.” to “As a result, a new approach for interoperability was introduced in PMML version 3.2.”

MiningSchema

Added frequencyWeight and analysisWeight as new options for usageType. They will not affect scoring, but will make model information more complete.

ModelComposition — No longer used, replaced by MultipleModels

ModelExplanation

New addition to PMML 4.0 that contains information to explain the models, model fit statistics, and visualization information.

ModelVerification

No changes

MultipleModels

Replaces ModelComposition. Important additions are segmentation and ensembles.

Added reference to ModelExplanation element in the model XSD

NaïveBayes

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

NeuralNetwork

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

Output

Extended output type to include Association rule models. The changes add a number of new attributes: “ruleFeature”, “algorithm”, “rank”, “rankBasis”, “rankOrder” and “isMultiValued”. A new enumeration type “ruleValue” is added to the RESULT-FEATURE

Regression

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

RuleSet

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

Sequence

Changed version to “4.0” from “3.2” in the example(s)

Statistics

accommodate weighted counts by replacing INT-ARRAY with NUM-ARRAY in DiscrStats and ContStats

change xs:nonNegativeInteger to xs:double in several places

add new boolean attribute ‘weighted’ to UnivariateStats and PartitionFieldStats elements

add new attribute cardinality in Counts

Also some very long lines in this document are now wrapped.

SupportVectorMachine

Added optional attribute threshold

Added optional attribute classificationMethod

Attribute alternateTargetCategory removed from SupportVectorMachineModel element and moved to SupportVectorMachine element

Changed the example slightly

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

Targets

No changes

Taxonomy

Changed: “A TableLocator may contain any description which helps an application to locate a certain table. PMML 3.2 does not yet define the content. PMML users have to use their own extensions. The same applies to InlineTable.” to “A TableLocator may contain any description which helps an application to locate a certain table. PMML standard does not yet define the content. PMML users have to use their own extensions. The same applies to InlineTable.”

Text

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

TimeSeriesModel

New addition to PMML 4.0 to support Time series models

Transformations

No changes

TreeModel

Changed version to “4.0” from “3.2” in the example(s)

Added reference to ModelExplanation element in the model XSD

Sources

http://www.dmg.org/v4-0/GeneralStructure.html

http://www.dmg.org/v4-0/Changes.html

and here are some companies using PMML already

http://www.dmg.org/products.html

I found the tool at http://www.dmg.org/coverage/ much more interesting though (see screenshot).

Zementis who we have covered in the interviews has played a steller role in bring together this common standard for data mining. Note Kxen model is also highlighted there.