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How can MARS® models be implemented for predictive purposes?

A MARS® predictive model can be implemented in two ways. First, new databases can be scored directly by identifying the MARS model and the data to be scored. MARS will perform all the required data transformations and calculations automatically and output the predicted scores. Second, the MARS predictive equation can be exported as ready-to-run C and SAS®-compatible code that can be deployed in the user's application framework.

How does MARS® construct its models?

MARS® starts from the premise that most relevant variables affect the outcome in a complex way. Therefore, when MARS considers whether to add a variable, it simultaneously searches for appropriate break points – knots. Models are constructed in a two-phase procedure. Phase I tests variables and potential knots, resulting in an overfit model. Phase II eliminates redundant factors and components that do not stand up to testing.

How does MARS® differ from conventional regression?

Conventional regression models typically fit straight lines to data. MARS® approaches model construction more flexibly, allowing for bends, thresholds, and other departures from straight-line methods. MARS builds its model by piecing together a series of straight lines with each allowed its own slope. This permits MARS to trace out any pattern detected in the data.

How does MARS® ensure that a model will perform as claimed on future data?

Almost all modeling technologies can track training data accurately. MARS® protects users from misleading results through its two-stage modeling process. MARS overfits its model initially but then prunes away all components that would not hold up with new data. MARS provides assessments through use of one of two built-in testing regimens: cross validation or reference to independent test data. Using these tests, MARS determines the degree of accuracy that can be expected from the best predictive model.

How does MARS® handle missing values?

MARS® automatically creates a missing value indicator – a dummy variable – that becomes one of the available predictors. These dummy variables represent the absence or the presence of data for the predictor variables in focus.

Introduction to Tree-Based Machine Learning

The following videos cover the underlying methods in the SPM® 8.2 Software Suite and provide demonstrations of each of the modeling engines.

Software Featured in the Videos:

  • SPM® 8.2 Software Suite
  • CART® Software
  • RandomForests® Software
  • TreeNet® Software
  • MARS® Software
  • RuleLearner™ Software
  • ISLE© Software
  • GeneralizedPathSeeker™ Software

MARS® - Multivariate Adaptive Regression Splines


Automatic Non-Linear Regression

The MARS® modeling engine is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions. The MARS methodology’s approach to regression modeling effectively uncovers important data patterns and relationships that are difficult, if not impossible, for other regression methods to reveal. The MARS modeling engine builds its model by piecing together a series of straight lines with each allowed its own slope. This permits the MARS modeling engine to trace out any pattern detected in the data.

High-Quality Regression and Classification

The MARS Model is designed to predict numeric outcomes such as the average monthly bill of a mobile phone customer or the amount that a shopper is expected to spend in a web site visit. The MARS engine is also capable of producing high quality classification models for a yes/no outcome. The MARS engine performs variable selection, variable transformation, interaction detection, and self-testing, all automatically and at high speed.

High-Performance Results

Areas where the MARS engine has exhibited very high-performance results include forecasting electricity demand for power generating companies, relating customer satisfaction scores to the engineering specifications of products, and presence/absence modeling in geographical information systems (GIS).



MARS® Supported File Types

MARS Supported File Types

The MARS® data-translation engine supports data conversions for more than 80 file formats, including popular statistical-analysis packages such as SAS® and SPSS®, databases such as Oracle and Informix, and spreadsheets such as Microsoft Excel and Lotus 1-2-3.


Saving MARS® Regression Spline Basis Functions to a New Dataset

MARS® (Multivariate Adaptive Regression Splines), introduced by Stanford University data mining guru Professor Jerome H. Friedman in 1988, is one of the landmarks in the evolution of regression methods. For the first time analysts could leverage a search mechanism intended to automatically discover nonlinearity and interactions in the context of classical regression.

Software Demonstrations

resources software demonstrations

The videos contains the demonstrations of the techniques using the SPM® Software Suite. Software Featured in the Videos: SPM® Software Suite, CART® Software, Random Forests® Software, TreeNet® Software, MARS® Software, RuleLearner® Software, ISLE© Software, Generalized PathSeeker™ Software.

What is MARS®?

Multivariate Adaptive Regression Splines was developed in the early 1990s by world-renowned Stanford physicist and statistician Jerome Friedman. It is an innovative, flexible modeling tool that automates the building of accurate predictive models for continuous and binary dependent variables.
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