The CART (Classification And Regression Trees) algorithm builds a decision tree by classifying a set of records.
The decision tree provides a model for classifying new samples.
The CART algorithm is a decision tree-based algorithm that seeks to locally divide data into smaller segments based on different values and combinations of predictors.
The CART algorithm identifies the best performing divisions and repeats this process regularly until the ideal result is achieved.
The result is a decision tree represented by a series of binary divisions resulting in terminal nodes which can be described by a set of specific rules.
The decision tree thus generated is easy to interpret and allows relevant conclusions to be drawn.
Designed for all types of users, the CART algorithm model reveals important relationships where other analysis tools fail.
The CART algorithm stands out from other predictive analysis solutions thanks to its unique and relevant methodology which allows automation, ease of use, performance and precision.
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