PMML Powered
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
K-Means
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 post-processing
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 post-processing
Text Mining
PMML 4.2 PMML 4.2 Regression Models (Linear and Logistic)
Decision Trees
Neural Networks
k-Nearest Neighbors
Naïve Bayes
Random Forest
Time Series
Association Rules (Producer Only)
Transformations
Multiple Models
PMML 3.0 through 4.3 General Regression
Regression
Neural Network
Ruleset
Trees
Scorecards
Vector Machine
Multiple Models: Model Composition, Ensembles and Segmentation
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
k-Nearest 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
k-Nearest 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
k-Nearest 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
k-Nearest 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 (center-based and distribution-based)
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)
k-Nearest 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 (Distribution-based)
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 post-processing
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
k-NN
Neural Network
Regression
Rule Sets
Scorecards
SVM
PMML 3.0 through 4.2 Association Rules
Decision Trees
Clustering
General Regression
Mining Model
Naïve Bayes
k-NN
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
k-Nearest 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 Center-Based)
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
K-Medoids
K-Means
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 user-defined 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 post-processing
Text Mining
PMML 4.2 Clustering Models
K-Means
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, Pre-post-processing
Teradata Warehouse Miner v5.3.1 PMML 2.1 through 3.2 Regression Models,
Decision Trees,
Neural Networks,
Clustering Models (center-based),
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
Zementis
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 post-processing
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 post-processing
Text Mining
e-mail info at dmg.org