Classification and Regression Trees

Classification and Regression Trees

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CART Treeviewer Add-on

TreeViewer is a Windows application designed to display and print trees developed on a UNIX workstation or server. The command files, data, and output files all initially reside on the UNIX platform, and the UNIX machine will do all the work of growing, pruning and selecting a tree. The Tree Navigator file, automatically generated by the UNIX version, is then opened directly on the PC. Using the point-and-click interface, you can interactively inspect detailed node reports and overall tree summary reports (including gains charts, variable importance tables, misclassifcation tables, and prediction success tables) for each size tree in the pruned sequence. You can also take advantage of the tree printing functionality otherwise accessible only in the regular GUI version of CART.



As an affordable add-on module to all CART for UNIX versions, TreeViewer brings your CART trees to life, TreeViewer:

  • Facilitates interactive investigation of the tree,
  • provides easy access to numerous reports,
  • allows inspection of any of the sub-trees in the sequence of pruned trees in a single CART run, and
  • facilitates report quality printing of decision trees.

To better understand the TreeViewer, download our CART evaluation version and check out CART's Navigator window and its exploration capabilities.

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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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SPM® Scalability

A user's license sets a limit on the amount of learn sample data that can be analyzed. The learn sample is the data used to build the model. Note that there is no limit to the number of test sample data points that may be analyzed. In other words, rows -by- columns of variables and observations used to build the model. Variable not used in the model do not count. Observations reserved for testing, or excluded for other reasons, do not count.

For example, suppose our 32MB version that sets a learn sample limitation of 8 MB. Each data point occupies 4 bytes. For instance, a 8MB capacity license will allow up to 8 * 1024 * 1024 / 4 = 2,097,152 learn sample data points to be analyzed.A data point is a represented by a 1-variable by- 1-observation (1-row by-1-column).

The following is a table that describes the current set of "sizes" available. Please note that the minimum required RAM is **not** the same as the learn sample limitation.

Size Data Limit MB Data Limit # of values  
minimum required
physical memory
(RAM) in MB
Licensed learn sample
data sizein MB 
(1 MB = 1,048,576 bytes)
Licensed # of learn
sample values
(rows by columns)
 
32 8 2,097,152  
64 18 4,718,592  
128 45 11,796,480  
256 100 26,214,400  
512 200 52,428,800  
1024 400 104,857,600  
2048 800 209,715,200 **64-bit only
3072 1200 324,572,800 **64-bit only

Additional larger capacity is available under 64-bit operating systems, using our non-GUI (command-line) builds. The non-GUI is very flexible and can be licensed for large data limits not currently available in the GUI product line. The current MAXIMUM is 8-GIG data capacity for our non-GUI builds.

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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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