Webcast: Predictive Analytics at the Speed of Business

James Taylor, CEO of Decision Management Solutions, and Dr. Alex Guazzelli, VP Analytics at Zementis, recently presented a webinar focusing on decision management and predictive analytics deployment, real-time use, and integration.

Click HERE to watch on-demand replay of the webinar.

Case studies and a live demo cover:

• The current challenges in getting a return on your predictive analytic investment
• The role of decision management in applying analytics when and where they are needed
• The roles of predictive analytics and business rules technologies decision management
• How real-time infrastructure and rapid deployment maximizes analytic value
• The importance of continuous monitoring and improvement in delivering ongoing results

R PMML Support: BetteR than EveR!

PMML, the Predictive Model Markup Language, has become the de-facto standard to represent not only predictive models, but also data pre- and post-processing. In so doing, it allows for the interchange of models among different tools and environments, avoiding proprietary issues and incompatibilities. R PMML Package
The PMML Package exports a variety of predictive models form R to PMML. The PMML package itself was conceived at first as part of Togaware's data mining toolkit Rattle. Although it can easily be accessed through Rattle's GUI, it can also be accessed directly in R.
To download the PMML Package from CRAN, the R Archive, click HERE. Extended PMML Support Traditionally, the PMML Package offered support for the following data mining algorithms: ksvm(kernlab): Support Vector Machines nnet: Neural Networks rpart: C&RT Decision Trees lm & glm (stats): Linear and Binary Logistic Regression Models arules: Association Rules kmeans and hclust: Clustering Model Recently, it has been expanded to support: multinom (nnet): Multinomial Logistic Regression Models; glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions randomForest: Random Forest Models for classification and regression coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards ada: Stochastic Boosting naiveBayes (e1071): Naive Bayes Classifiers svm (e1071): Support Vector Machines Once exported in PMML, your R model can be readily deployed in the Zementis ADAPA Scoring Engine, where it can be put to work immediately.

Model Ensemble in PMML

Once a solution used by just a few data scientists, model ensembles are now being used to solve more and more problems. That's due in part to the highly publicized Netflix prize and in part to the many tools that now make it easier for users to develop a solution containing multiple models.

In a model ensemble situation, every model is executed and the overall result or output is a combination of the partial results obtained from each model.


PMML is capable of representing not only model ensembles, but also composition, segmentation, and chaining. The same is true for ADAPA, which can consume PMML files containing multiple models.

For PMML Examples, including an example file for Random Forest Models, please refer to the DMG PMML Examples page.