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.

What makes CART® so easy to interpret?

As illustrated above, the results of a decision-tree data-mining project are displayed as a tree-shaped visual diagram. Discovered relationships and patterns in the data - even in massively complex datasets with hundreds of variables - are presented as a flow chart. Compare this to complex parameter coefficients in a logistic regression output or a stream of numbers in a neural-net output, and the appeal of decision trees is readily apparent.

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How are CART®'s decision trees grown?

CART uses strictly binary, or two-way, splits that divide each parent node into exactly two child nodes by posing questions with yes/no answers at each decision node. CART searches for questions that split nodes into relatively homogenous child nodes, such as a group consisting largely of responders, or high credit risks, or people who bought sport-utility vehicles. As the tree evolves, the nodes become increasingly more homogenous, identifying important segments. Other methods, such as CHAID, favor multi-way splits that can paint visually appealing trees but that can bog models down with less accurate splits.

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What is a decision tree?

A decision tree is a flow chart or diagram representing a classification system or predictive model. The tree is structured as a sequence of simple questions, and the answers to these questions trace a path down the tree. The end point reached determines the classification or prediction made by the model, which can be a qualitative judgment (e.g., these are responders) or a numerical forecast (e.g., sales will increase 15 percent).

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