Classification and Regression Tree (CART) Algorithm in Data mining
/ September 25, 2017

CART Algorithm overview A CART tree is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly, beginning with the root node that contains the whole learning sample. Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. Decision Trees are commonly used in data mining with the objective of creating a model that predicts the value of a target (or dependent variable) based on the values of several input (or independent variables).  In today’s post, we discuss the CART decision tree methodology. Classification Trees: where the target variable is categorical and the tree is used to identify the “class” within which a target variable would likely fall into. Regression Trees: where the target variable is continuous and tree is used to predict its value. The CART decision tree is a binary recursive partitioning procedure capable of processing continuous and nominal attributes as targets and predictors. Data are handled in their raw form; no binning is required or recommended. Beginning in the root node, the data are split into two children, and…

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