Wednesday, February 27, 2008

Converting older versions of PMML - Supported modeling elements.

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

Friday, February 22, 2008

Can I export my Weka model into PMML?

Weka does not export models into PMML. However, it allows for certain models to be imported.

Can I export my SAS model into PMML?


Yes, you can. SAS Enterprise Miner exports PMML for a variety of modeling techniques, including neural networks. Please note that depending on the version of Enterprise Miner you have models are exported in an older version of PMML (PMML 2.1 or 3.1). These are automatically converted to latest version of PMML once uploaded into ADAPA.

If you only have the base SAS product, you will probably need to export your model to PMML by writing your own script. Feel free to contact us for tips and help on how to do that.

Thursday, February 21, 2008

How can I export PMML from SPSS Statistics?

In newer versions of SPSS Statistics (I believe starting with version 14), you can export many different modeling techniques into PMML. For some techniques, it also exports the transformations that are applied to the input data before the model is built.

In SPSS Statistics version 16 and beyond, you can export PMML for neural networks (back-propagation and radial-basis) by selecting the Export tab on the model building menu. Note that scaling of numerical variables and dummy-fication of categorical variables is expressed in the resulting PMML file under the TransformationDictionary element.

Tuesday, February 19, 2008

How can I represent scaling of numerical variables in PMML?

You can scale numerical variables by using transformations in PMML. These can be part of the TransformationDictionary or LocalTransformations elements. For neural networks, numerical transformations can also be done in the NeuralInputs element (as well as the NeuralOutputs element).

For example, the transformation element NormContinuous can be used to implement simple normalization functions such as the z-score transformation (X - m ) / s, where m is the mean value and s is the standard deviation.

Pleaser, refer to the transformations page of the dmg website for PMML examples.

Monday, February 11, 2008

Can I upload multiple models into ADAPA?


Yes, you can. ADAPA supports deployment of multiple models.

Once your model(s) is uploaded successfully, it is ready to be used, either via the ADAPA Console and the Excel add-in in batch-mode or through Web Services in real-time.

If you have a model already in place, ADAPA will throw an error if you try to upload another model with the same name (the name of a model is specified inside the PMML file in its model element).

ADAPA also supports composing of multiple models into a single model. This important feature supports a variety of model composition cases such as model segmentation, composition, ensembles, and chaining.

For examples and instructions on how to represent model composition in PMML and ADAPA, please refer to the book "PMML in Action" available at amazon.com.

What happens if my data contains records with missing values? Will ADAPA score such records anyway?


If an input value is missing in a given data record and the value is part of an active PMML input variable (as defined in the Mining Schema element), then ADAPA will try to replace the missing value by the replacement value specified in the Mining Schema. So, if you get a score back for a data record containing missing values, that's because ADAPA is replacing the missing values by the replacement values specified in your PMML file.

I mentioned "try" before because you may have not specified a replacement value in the mining schema. If that is the case, ADAPA will not produce a score for the given data record with missing data.

This is slightly different than what is implied by PMML itself (see the mining schema PMML element), but we feel it gives the user better control over what ADAPA should do in case of missing values. In this way, if your model is a neural network model, for example, you will need to explicitly define the replacement value to be zero for every input if that is what you want. This is in contrast to having ADAPA do that in an automatic way for every type of modeling technique.

Click here to learn more on how missing values are handled in decision trees.

Friday, February 8, 2008

ADAPA refuses to upload my model, what should I do?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

How can I test my model once it is successfully uploaded into ADAPA?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

Does ADAPA support all aspects of the Neural Network PMML element?

Almost all. ADAPA does not support Neural Networks with recurrent connections.

What is PMML and how can I learn more about it?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

What is ADAPA anyway?

ADAPA (Adaptive Decision And Predictive Analytics) is a predictive decisioning platform. It combines the power of predictive analytics and business rules to facilitate the tasks of managing and designing automated decisions systems.


As a scoring engine, ADAPA supports the PMML standard (versions 2.0 to 4.2). In this way, different data mining models can be uploaded into the engine and executed in real-time or batch mode.

For a more detailed list of features, feel free to take a look at the ADAPA page. If you are still unsure about any of the features or would like to learn more about it, drop us a note or give us a call. You can find our contact information in the contacts page of the Zementis website.

What kind of activation functions for Neural Networks are supported by ADAPA?

ADAPA supports all the PMML list of activation functions for the Neural Network model element.

In PMML, activations functions are divided into two groups. Group 1 contains the following functions:
  • threshold
  • logistic
  • tanh
  • identity
  • exponential
  • reciprocal
  • square
  • Gauss
  • sine
  • cosine
  • Elliott
  • arctan
Group 2 contains only one function:
  • radialBasis
For more details, please take a look, for example, at the PMML 4.0 Neural Network specification.

What types of Neural Network models built with R nnet can I export to PMML?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

Does ADAPA support all general regression PMML models?

Yes, ADAPA supports the entire list of general regression PMML model elements. These are:
  • regression
  • generalLinear
  • multinomialLogistic
  • ordinalMultinomial
  • generalizedLinear
  • CoxRegression
ADAPA also supports all the link and cumulative link functions as defined in PMML 4.0 for general regression models.

Note that if you export regression models from SPSS, these will be in the general regression format.

SVM element in PMML allows for multiclass-classification

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

What types of regression models built with R can I export to PMML?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

What types of SVM models built with R ksvm can I export to PMML?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

Thursday, February 7, 2008

How can I export PMML code from R?

This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

How can I export PMML code from older versions of SPSS?

In older versions of SPSS, like SPSS 11.5, a linear regression model can be exported to PMML by going through the following sequence of menus: Analyze -> Regression -> Linear... -> Save... You will find yourself in box "Linear Regression: Save". Enter the file name and location you want the PMML file to be written to in "Export model information to XML file" at the bottom of the "Save" box.

After the model is trained, a file will be created in the specified location containing a PMML representation of your linear regression model. A similar sequence of actions and results should work for Multinomial Logistic models.

Important things to notice about the PMML file:

1) The model is represented as a general regression PMML element;

2) For SPSS versions up to version 14, data transformations are not part of the PMML file. Therefore, you will need to add any data transformations manually. You can use the Transformation Generator tool to graphically design your transformations and then paste the resulting PMML code into your file.





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