Sydney, Australia Training
June 5-7, 2006

Sharpen your expertise!
And in-depth courses available for attendees who are new to data mining.

Data Mining with Decision Trees
Predictive Modelling with Automated Regression Tools
TreeNet: Stochastic Gradient Boosting
RandomForests

Data Mining with Decision Trees
8:00AM-5:00PM June 5, 2006

Discover the power of tree structured data mining during this popular intro tutorial by Dan Steinberg, one of the world's leading experts in CART (classification and regression tree) technology and real world applications. This tutorial is geared toward statisticians and IT audiences who are interested in understanding the conceptual basis of decision tree technology what it is, why it works, how it has been used, and how it can help you make better business decisions. Explore the practical use and application of decision trees in solving real world data mining problems and learn about:

• Decision tree fundamentals

• Decision tree applications

• How to build and interpret CART models

• How to use advanced options
• alternative splitting rules,
• prior probabilities, and
• differential costs of misclassification
• to construct more robust predictive models

Predictive Modelling with Automated Regression Tools
8:00AM-5:00PM June 6, 2006

This tutorial is for data analysts and modelers interested in learning about MARS, an automated non-linear regression data mining tool developed by one of CART’s originators.

Be the first on your block to get answers about this exciting, predictive modelling tool: What is MARS? Why does it work? How can it be used? How can it help you develop more accurate regression models for problems such as predicting credit card holder balances, insurance claim losses, customer catalog orders, and cell phone usage. You will learn:

• How to plan and execute a MARS analysis

• How to refine models using key control parameters

• How to use MARS to improve existing models

• How to hybridize MARS and CART to gain that extra lift.

TreeNet: Stochastic Gradient Boosting
8:00AM-1:00PM June 7, 2006

TreeNetT stochastic gradient boosting is Jerome Friedman's latest advance in data mining methodology. In TreeNetT, classification and regression models are built up gradually through a potentially large collection of small trees, each of which improves on its predecessors through an error-correcting strategy. Individual trees may be as small as one split, but the final models can be extraordinarily accurate and are remarkably resistant to overfitting. Innovations in this methodology include (a) never using all the training data at any one time, (b) a very slow learning rate, and (c) potentially ignoring increasingly large portions of the training data as the model evolves. Although the TreeNetT model is normally very complex, new graphical tools and diagnostics assist the user in interpreting the results.

RandomForests
2:00PM-5:00PM June 7, 2006

Random Forests, Leo Breiman's latest data mining technology, is based on learning ensembles of CART trees. By judiciously injecting randomness into the tree building process and then combining hundreds of these trees, RF is able to deliver high performance predictive models and a variety of novel exploratory data analysis results. RF also incorporates new metric free CLUSTER analyses that automatically select the variables used to define each cluster, with potentially different variables defining each cluster.

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