For beginners and expert users

  • General introductory videos to SPM's data mining.

  • Comprehensive training videos

  • Webinars & Tutorials: Tips & Tricks and industry specific insights

 
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    For beginners and expert users

    • General introductory videos to SPM's data mining.

    • Comprehensive training videos

    • Webinars & Tutorials: Tips & Tricks and industry specific insights

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CART®

104Frequently Asked Questions for CART®

CART® is the ultimate classification tree that has revolutionized the entire field of advanced analytics and inaugurated the current era of data mining. CART, which is continually being improved, is the most important tool in modern data mining methods. Designed for both non-technical and technical users, CART can quickly reveal important data relationships that could remain hidden using other analytical tools.

CART is based on landmark mathematical theory introduced in 1984 by four world–renowned statisticians at Stanford University and the University of California at Berkeley. Salford Systems' implementation of CART is the only decision tree software embodying the original proprietary code. The CART creators continue to collaborate with Salford Systems to enhance CART with proprietary advances.

CART® and Large Datasets

CART is capable of determining the number of records in your data sets, and uses this information to predict the memory and workspace requirements for trees that you build. Also, CART will read your entire data set each time a tree is built. At times these actions may be problematic, especially if you have enormous data sets.

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What are CART®'s "automatic self-validation procedures"?

CART uses two test procedures to select the "optimal" tree, which is the tree with the lowest overall misclassification cost, thus the highest accuracy. Both test disciplines, one for small datasets and one for large, are entirely automated, ensuring that the optimal tree model will accurately classify existing data and predict results.

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What is a "multiple-tree, committee-of-expert method," or "bootstrap aggregation"?

The use of multiple trees in a committee of experts is a relatively new technique, and one of CART's creators has developed a dramatically effective way of combining trees in CART. Prediction errors can be reduced as much as 50 percent by directing CART to draw 50 or more different random samples from the training data, grow a different tree on each sample, and then allow the different trees to "vote" on the best classification. When appropriate, combining trees can yield a substantial performance edge over any other data mining procedure.

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What are "intelligent surrogates for missing values"?

CART handles missing values in the database by substituting "surrogate splitters," which are back-up rules that closely mimic the action of primary splitting rules. Suppose that, in a given model, CART splits data according to household income. If a value for income is not available, CART might substitute education level as a good surrogate.

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