CART® software 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. Others have tried to copy CART, but no one has succeeded, as evidenced by accuracy, performance, feature set, built-in automation and ease of use. Designed for both non-technical and technical users, CART can quickly reveal important data relationships that could remain hidden using other analytical tools. Proprietary Code: Technically, 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 continually enhance CART with proprietary advances. Fast and Versatile: Patented extensions to CART are specifically designed to enhance results for market research and web analytics. CART supports high-speed deployment, allowing Salford models to predict and score in real time on a massive scale. Over the years CART has become known as the fastest and most versatile predictive modeling algorithm available to analyst, it is also used as a foundation to many modern data mining approaches based on bagging and boosting

References in zbMATH (referenced in 7 articles )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui: Statistical foundations of data science (2020)
  2. Sagan, Adam; Łapczyński, Mariusz: SEM-tree hybrid models in the preferences analysis of the members of Polish households (2020)
  3. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  4. Yukinobu Hamuro; Masakazu Nakamoto; Stephane Cheung; Edward Ip: mbonsai: Application Package for Sequence Classification by Tree Methodology (2018) not zbMATH
  5. Iliev, Iliycho Petkov; Voynikova, Desislava Stoyanova; Gocheva-Ilieva, Snezhana Georgieva: Application of the classification and regression trees for modeling the laser output power of a copper bromide vapor laser (2013) ioport
  6. Chiu, Chih-Chou; Hwang, Shin-Ying; Cook, Deborah F.; Luh, Yuan-Ping: Process disturbance identification through integration of spatiotemporal ICA and CART approach (2010) ioport
  7. Kellie Archer: rpartOrdinal: An R Package for Deriving a Classification Tree for Predicting an Ordinal Response (2010) not zbMATH