PMML Sample Models:

The PMMLs provided below are examples of predicted models developed that use the PMML standard.  These samples are not intended for performance or vendor comparisons as they are provided solely for users to gain a better understanding of PMML.  No representation is made as to the accuracy and applicability of these models.   Also included are the datasets used to train and validate these predictive models. 

The Data Mining Group is always looking to increase the variety of these samples.  If you would like to submit samples, please see the instructions below.

Datasets for PMML Sample Models

These datasets are used in conjunction with the sample PMML models.  While a high level description is provided here, more details can be found in the ReadMe text file associated with each dataset.  If you publish material based on these datasets, please note the source in your acknowledgements.

Additional PMML Examples

These models are additional examples of PMML, not based on the datasets listed above (datasets marked * can be found by seaching the UCI Machine Learning Repository, datasets marked N/A are not available).  These models are included here to provide a wider range of PMML examples for inspection and understanding.

How to Submit PMML Models:

If you wish to provide PMML models, please send the following to info@dmg.org.  In the body of your message please provide:

• Text describing the model, including (* = unless included in the PMML Header element):

  PMML Version *

  Application *

  Application Version *

  Submitting Organization

  Any special characteristics

• If you do not use one of the datasets already listed here, please provide text describing the dataset, including

  Dataset Title

  Source(s), including any acknowledgements:
  Any past usage
  Description and other relevant Information:
  Number of Records (items, occurrences, rows, etc.)
  Number of Variables (fields, columns, etc.)
  Variable Information, especially data types, categorical values, valid ranges, etc.
  Missing Values Descriptions
  Summary Statistics & Data Distributions

  Output/Scoring Information

• If you do use one of the datasets already listed here, please provide the output of your model for inclusion with the existing dataset.
• Contact information, and whether you want your information included on this webpage

Also, attach the following files:

• The PMML model
• The dataset used to train and validate the model.  Also, include in the dataset the output of the model so other users can verify their results.  The first line (row) should contain the variable names (column headers)

Acknowledgements:

The Data Mining Group thanks the UCI Repository of Machine Learning Databases for being a valuable resource:

Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.