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Classification and Regression Trees

CART 6.0 ProEX Features

CART 6.0 ProEX Features

CART 6.0 ProEX Download

CART 6.0 ProEX, released in 2008, comes with a huge list of new features that will help analysts work more rapidly and guide their models to the best-performing trees. This is a dramatic upgrade of our flagship product and is drawing rave reviews from our customers. All of the new CART 6.0 ProEX features are explained in detail in our feature matrix (PDF) some highlights are listed below:

Tree Controls

  • Force splitters into nodes
  • Confine select splitters to specific regions of a tree (Structured Tree™)

HotSpot Detector™

  • Search data for ultra-high performance segments.
  • HotspotDetector trees are specifically designed to yield extraordinarily high-lift or high-risk nodes. The process focuses on individual nodes and generally discards the remainder of the tree.

Train/Test Consistency Assessment

  • Node-by-node summaries of agreement between train and test data on both class assignment and rank ordering of the nodes.
  • Quickly identifies ideally-performing robust trees.

Modeling Automation

  • Automatically generates entire collections of trees exploring different control parameters.
  • Nineteen automated batteries cover exploration of multiple splitting rules, five alternative missing value handling strategies, random selection of alternative predictor lists, progressively smaller (or larger) training sample sizes, and much more.

Predictor Refinement

  • Includes stepwise backwards predictor elimination using any of three predictor ranking criteria (lowest variable importance rank, lowest loss of area under the ROC curve, highest variable importance rank).

Model Assessment via Monte Carlo Testing

  • Measures possible overfitting with automated Monte Carlo randomization tests.

Constructed Features

  • New tools for automatic construction of new features (as linear combinations of predictors).
  • Identification of multiple lists of candidates allows precise control over which predictors may be combined into a single new feature.

Unsupervised Learning Mode

  • Uses Breiman's column scrambler to automatically detect potential clusters with no need to scale data, address missing values, or select variables for clustering.

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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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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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