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CART Classification And Regression Trees®

CART

CART® - Classification and Regression Trees

Ultimate Classification Tree:

Salford Predictive Modeler’s CART® modeling engine is the ultimate classification tree that has revolutionized the field of advanced analytics, and inaugurated the current era of data science. CART is one of the most important tools in modern data mining.

Proprietary Code:

Technically, the CART modeling engine is based on landmark mathematical theory introduced in 1984 by four world-renowned statisticians at Stanford University and the University of California at Berkeley. The CART Modeling Engine, SPM’s implementation of Classification and Regression Trees, is the only decision tree software embodying the original proprietary code.

Fast and Versatile:

Patented extensions to the CART modeling engine are specifically designed to enhance results for market research and web analytics. The CART modeling engine supports high-speed deployment, allowing Salford Predictive Modeler’s models to predict and score in real time on a massive scale. Over the years the CART modeling engine has become known as one of the most popular and easy-to-use predictive modeling algorithms available to the analyst, it is also used as a foundation to many modern data mining approaches based on bagging and boosting.

 

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CART®

CART® - Classification and Regression Trees

CART® Supported File Types

CART Supported File Types

The CART® 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.

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Model Deployment

Any CART model can be easily deployed when translated into one of the supported languages (SAS®-compatible, C, Java, and PMML) or into the classic text output. This is critical for using your CART trees in large scale production work.

The decision logic of a CART tree, including the surrogate rules utilized if primary splitting values are missing, is automatically implemented. The resulting source code can be dropped into external applications, thus eliminating errors due to hand coding of decision rules and enabling fast and accurate model deployment.

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