Company / Project

Software

PMML Producer

PMML Consumer

Supported
Model Type




PMML 3.0 through 4.2 
Decision Trees



PMML 4.1 

Regression Models (Linear and Logistic)
Naïve Bayes
KMeans
Alpine Forest
SVM


KnowledgeSTUDIO

PMML 3.2 

Decision Trees
Regression Models (Linear and Logistic)
Neural Networks
Clustering Models
Rule Set Models (Scorecards)

KnowledgeSEEKER

PMML 3.2 

Decision Trees

StrategyBUILDER

PMML 3.2 

Decision Trees (Strategy Trees)


BigML Public API

PMML 4.1


Decision Trees (classification and regression)



PMML 4.2

PMML 4.2

Scorecards
Regression Models (Linear and Logistic)
Segmented Scorecard Models
Decision Tree (Ensemble)


Strategy Tree Optimization

PMML 3.0, 3.1 
PMML 3.0, 3.1 
Decision Tree 
PowerCurve™ Strategy Management

PMML 3.0, 3.1, 4.0, 4.1 
PMML 3.0, 3.1, 4.0, 4.1 
Decision Tree 

PMML 3.0, 3.1, 4.0, 4.1 
Regression Model 


PMML 4.1, 3.2, 3.0 

Scorecards
Regression Models (Linear and Logistic)
Neural Networks
Decision Trees
Segmented Scorecard Models
Boosted Tree Ensembles
All R PMML produced models (see R/Rattle entry below)

Analytic Modeler Scorecard

PMML 4.1 

Scorecards
Boosted Tree Ensembles


PMML 4.2, 3.2 

Scorecards

Decision Optimizer

PMML 3.2 
PMML 3.2 
Consumes:
Scorecards
Regression Models (Linear and Logistic)
Neural Networks
Decision Trees
Produces:
Decision Trees

Blaze Advisor


PMML 3.2, 4.2 
Scorecards
Regression Models (Linear and Logistic)
Neural Networks
Decision Trees
Random Forests



PMML 2.0 through 4.2 
Cluster
General Regression
Naïve Bayes
Neural Networks
Regression
Ruleset
Scorecard
Trees
Vector Machines



PMML 2.0 through 4.2 
Decision Trees
Support Vector Machines
Mining Models
Neural Networks
Regression & General Regression
Clustering Models
Naïve Bayes
Ruleset Models
Association Rules
Scorecards
Pre and postprocessing
Text Mining



PMML 3.2, 4.2 
Scorecards
Regression Models (Linear and Logistic)




PMML 2.0 through 4.2 
Decision Trees
Support Vector Machines
Mining Models
Neural Networks
Regression & General Regression
Clustering Models
Naïve Bayes
Ruleset Models
Association Rules
Scorecards
Pre and postprocessing
Text Mining



PMML 4.2 
PMML 4.2 
Regression Models (Linear and Logistic)
Decision Trees
Neural Networks
kNearest Neighbors
Naïve Bayes
Random Forest
Time Series
Association Rules (Producer Only)
Transformations
Multiple Models




PMML 3.0 through 4.2 
Decision Trees
Regression
Scorecards


IBM SPSS Statistics 21

PMML4.0

PMML2.0 through 4.0

Clustering Models
Decision Trees
General Regression
MiningModel
Neural Networks
Time Series
Naïve Bayes (produced only by SPSS Statistics Server)
Also consumes Ruleset, Regression, and Support Vector Machine Models

IBM SPSS Statistics 22

PMML4.1

PMML2.0 through 4.1

Clustering Models
Decision Trees
General Regression
kNearest Neighbors
Mining Model
Neural Networks
Time Series
Naïve Bayes (produced only by SPSS Statistics Server)
Also consumes Ruleset, Regression, and Support Vector Machine Models

IBM SPSS Statistics 23

PMML4.1

PMML2.0 through 4.1

Association Model (only produced)
Clustering Models
Decision Trees
General Regression
kNearest Neighbors
Mining Model
Neural Networks
Time Series
Naïve Bayes (produced only by SPSS Statistics Server)
Also consumes Ruleset, Regression, and Support Vector Machine Models

IBM SPSS Modeler 15

PMML4.0

PMML2.0 through 4.0

Produces:
Association Model
Clustering Models
Decision Trees
Mining Model
Naïve Bayes
Neural Networks
Regression & General Regression Models
Ruleset Models
Support Vector Machines
Consumes (scores):
all of the above except Association, and Mining Model.

IBM SPSS Modeler 16

PMML4.1

PMML2.0 through 4.1

Produces:
Association Model
Clustering Models
Decision Trees
kNearest Neighbors
Mining Model
Naïve Bayes
Neural Networks
Regression & General Regression Models
Ruleset Models
Support Vector Machines
Consumes (scores):
all of the above except Association, and Mining Model.

IBM SPSS Modeler 17

PMML4.1

PMML2.0 through 4.1

Produces:
Association Model
Clustering Models
Decision Trees
kNearest Neighbors
Mining Model
Naïve Bayes
Neural Networks
Regression & General Regression Models
Ruleset Models
Support Vector Machines
Consumes (scores):
all of the above except Association.

IBM SPSS Collaboration and Deployment Services v. 5.0


PMML2.0 through 4.0

Clustering Models
Decision Trees
Naïve Bayes
Neural Networks
Regression & General Regression
Ruleset Models
Support Vector Machines

IBM SPSS Collaboration and Deployment Services v. 6.0


PMML2.0 through 4.1

Clustering Models
Decision Trees
Nearest Neighbors
Naïve Bayes
Neural Networks
Regression & General Regression
Ruleset Models
Support Vector Machines

IBM SPSS Collaboration and Deployment Services v. 7.0


PMML2.0 through 4.1

Clustering Models
Decision Trees
Nearest Neighbors
Naïve Bayes
Neural Networks
Regression & General Regression
Ruleset Models
Support Vector Machines

IBM Netezza Analytics v3.0

PMML 4.0


Association Rules
Clustering Models (centerbased and distributionbased)
Decision Tree Models
Naive Bayes

IBM InfoSphere Warehouse V9.7
IBM InfoSphere Warehouse V10.1
IBM DB2 Advanced Enterprise Server Edition V10.5
IBM DB2 Advanced Workgroup Server Edition V10.5

PMML 3.2 
PMML 2.0 through 3.2 
Association Rules Models
Sequence Models
Naïve Bayes Models
Logistic Regression Models

PMML 3.0 

Clustering Models (center and distribution based)
Regression Models
Decision Trees


PMML 2.0 through 3.0 
Clustering Models (center and distribution based)
Regression Models
Decision Trees
Neural Networks


KNIME
2.10

PMML
4.2

PMML
4.2

Neural
Networks
Regression and General Regression
Models
Clustering
Models
Decision
Trees
Naïve Bayes
Rule Set
Editor
Rule Set
Predictor
Support
Vector Machines including support for Transformation elements
Model Ensembles
And support for Transformation elements

KNIME
2.10 with R extension

PMML
4.1

PMML
4.1

All
R PMML produced models (see R/Rattle entry below) that are compatible to PMML 4.1

KXEN 
Analytic Framework 4.0.10, 5.0.7 and 5.1.2 
PMML 3.2 

Regression Models, Clustering Models, Mining Models 

Kamanja 

PMML 4.0 to 4.2 
Assocation rules (Association)
Cluster model (Clustering)
General regression (Regression, Cox regression, Classification)
kNearest neighbors (Regression, Classification, Clustering)
Naive Bayes (Classification)
Neural network (Regression, Classification (Except for the radialBasis value of the activationFunction attribute))
Regression (Regression, Classification)
Rule set (Classification)
Scorecard (Regression)
Tree model (Classification)
Support Vector Machine (SVM) (Regression, Classification)
Ensemble model (Regression, Classification, Clustering)

Microsoft 
SQL Server 
PMML 2.1 

Decision Trees (Classification), Clustering Models (Distributionbased) 

MicroStrategy
Data Mining Services
Versions
8.0 and above

PMML
3.0, 3.1, 4.0

PMML
2.0, 2.1, 3.0,3.1, 3.2, 4.0

Regression
Models (Consumer & Producer)
Decision
Trees (Consumer & Producer)
Mining
Models (Consumer & Producer)
Clustering
Models (Consumer & Producer)
Neural
Network Models (Consumer Only)
General
Regression Models (Consumer Only)
Support
Vector Machine Models (Consumer Only)
Rule
Set Models (Consumer Only)
Association
Rules (Consumer & Producer)
Time
Series Models (Consumer & Producer)




PMML 2.0 through 4.2 
Decision Trees
Support Vector Machines
Mining Models
Neural Networks
Regression & General Regression
Clustering Models
Naïve Bayes
Ruleset Models
Association Rules
Scorecards
Pre and postprocessing
Text Mining



PMML 4.3 
PMML 4.3 
Bayesian Networks


PMML 4.3 
PMML 4.3 
Gaussian Process Regression



PMML 4.3 
Gaussian Process Regression


Augustus 0.4x

PMML 4.0

PMML 4.0

Decision Trees
Regression
Naïve Bayes
Baseline (With Segmentation on all of the above)

Augustus 0.5x

PMML 4.1 
PMML 4.1 
Baseline
Clusters
Naïve Bayes
Regression
RuleSet
Trees (With Segmentation on all of the above)

Augustus 0.6x

PMML 4.1 
PMML 4.1 
Baseline
Clusters
Trees (With Segmentation on all of the above)



PMML 3.0 through 4.2 

All model types



PMML 3.0 through 4.2 
Association Rules
Decision Trees
Clustering
General Regression
Mining Model
Naïve Bayes
kNN
Neural Network
Regression
Rule Sets
Scorecards
SVM



PMML 3.0 through 4.2 
Association Rules
Decision Trees
Clustering
General Regression
Mining Model
Naïve Bayes
kNN
Neural Network
Regression
Rule Sets
Scorecards
SVM


Signal Hub 2.7

PMML 4.2

PMML 3.0 through 4.2

Produces
Decision Trees
Clustering
Regression Models
Naïve Bayes
Support Vector Machines
Neural Networks
Consumes
Association Rules
Decision Trees
Clustering
General Regression
Mining Model
Naïve Bayes
Neural Networks
Regression Models
Rule Sets
Scorecards
Support Vector Machines


Pega Platform 7.3.1


PMML 3.0 through 4.3

Cluster model
General regression
Mining (multiple/ensemble) model
Neural Network
kNearest Neighbors
Naïve Bayes
Ruleset
Regression
Support Vector Machine
Scorecard
Tree

Pervasive DataRush

Pervasive DataRush V5.0

PMML 3.2

PMML 3.2

Decision Trees (Consumer & Producer)
Regression Models (Consumer & Producer)
Clustering Models (kmeans CenterBased)
Association Rules (Producer)
Naïve Bayes (Consumer & Producer)




PMML 2.0 through 4.0

Decision Trees
Support Vector Machines
Neural Networks
Regression and General Regression
Clustering Models
Naiive Bayes


RapidMiner with PMML Extension

PMML 3.2, 4.0


Linear Regression
Logistic Regression Models
Decision Trees
Rules
KMedoids
KMeans
Naïve Bayes

R


PMML 4.3


kNN
Mining Models
Regression Models
General Regression Models (including Cox)
Neural Networks
Decision Trees
Clustering Models
Association Rules
Support Vector Machines
Multinomial Logistic Regression
Random Forest
Random Survival Forest
Naïve Bayes Classifiers
And support for merging of Transformation elements


PMML 4.3


Transformations

Salford Systems

CART
6.0

PMML 3.1


Decision
Trees
Composition

TreeNet
2.0 Pro

3.1


Composition

Mars
3.0

3.2


Simple
Regression (uses transformations and userdefined functions to implement
basis functions)


SAND CDBMS V6.1 PMML extension


PMML 3.1, 3.2

Association Rules
Clustering Models
Regression Models
Neural Networks
Naïve Bayes
Support Vector Machines
Ruleset Model
Decision Trees


SAS Enterprise Miner

PMML 2.1 (EM 5.1)
PMML 3.1 (EM 5.3)
PMML 4.0 (EM 7.1)
PMML 4.1 (EM 12.3 and 13.1)


Linear Regression
Logistic Regression
Decision Trees
Neural Networks
Clustering
Association Rules

SAS Base 9.3M2


PMML 4.0

Clustering
Neural Networks
Regression
Tree

SAS Base 9.40


PMML 4.1

Clustering
Neural Networks
Regression
Tree
Scorecard
General Regression

SAS Base V9.40M1


PMML 4.1

Clustering
Neural Networks
Regression
Tree
Scorecard
General Regression
Naïve Bayes
Support Vector Machine
Time Series




PMML 2.0 through 4.2 
Decision Trees
Support Vector Machines
Mining Models
Neural Networks
Regression & General Regression
Clustering Models
Naïve Bayes
Ruleset Models
Association Rules
Scorecards
Pre and postprocessing
Text Mining



PMML 4.2 

Clustering Models
KMeans
Regression Models (Linear, Ridge, Binary Logistic)
Lasso Model
SVM


Sparkling Logic SMARTS™ 

PMML 4.2 
Neural Networks
Support Vector Machines
Trees / Decision Trees
Naïve Bayes
Regression (Linear, Logistic, Multinomial) & General Regression
Clustering Models
Ruleset Models
Scorecards
Mining Models (incl. Random Forest)
Transformations, Prepostprocessing


Teradata Warehouse Miner v5.3.1 

PMML 2.1 through 3.2 
Regression Models, Decision Trees, Neural Networks,
Clustering Models (centerbased), Mining Models (regression types only)


TIBCO Spotfire 6.5

PMML 4.1


All R PMML produced models (see R/Rattle entry above)

TIBCO Enterprise Runtime for R 2.5

PMML 4.1 

All R PMML produced models (see R/Rattle entry above)

Weka (Pentaho)

Weka


PMML
3.2

Regression and General Regression
Neural Networks
Rule Set Models
Decision Trees



PMML
4.3


Decision
Trees
Random Forest Models
Clustering
Regression Models



PMML 2.0 through 4.3

Decision
Trees
Support
Vector Machines
Mining Models
Neural
Networks
Regression
& General Regression
Clustering Models
Naïve Bayes Ruleset Models
Association Rules Scorecards
Pre and postprocessing
Text Mining



PMML 2.0 through 4.3

Decision
Trees
Support
Vector Machines
Mining Models
Neural
Networks
Regression
& General Regression
Clustering Models
Naïve Bayes Ruleset Models
Association Rules Scorecards
Pre and postprocessing
Text Mining
