Introduction to Multivariate Adaptive Regression Splines (MARS)

Understand tree– regression using MARS, its advantages and disadvantages, piece–wise constant solutions and how it bridges the evolution of the regression component in CART.

Key Controls In MARS

Introduction to the core concepts of MARS:

• Adaptive modeling
• Smooths, splines and knots

Refining MARS models:

• Basis functions
• Handling of missing values
• MARS handling of interactions

MARS in Action

Develop more accurate regression models for problems such as predicting credit card holder balances, insurance claim losses, and customer catalog orders.

Guide to reading MARS output:

• Build a MARS model in SPM
• Understand the MARS interface
• Control parameters
• How MARS handles categorical variables
• How MARS handles binary responses

Introduction to Ensemble–Based Modeling Techniques

RandomForests®, created by Leo Breiman and Adele Cutler, 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.

Loose Ends and Application

Q&A with the experts for further discussion and apply SPM to your own data sets.