Performance Analysis of the CHAID Algorithm for Accuracy

The chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the most used supervised learning methods as it adaptable solving any kind problem at hand. We are keenly aware non-linear relationships among CHAID maps, and they can empower predictive models with stability. However, we do not precisely know how high its accuracy. To determine perfect scope fits into, this paper presented an analysis accuracy algorithm. introduced causes, applicable conditions, application algorithm, then highlight differences in branching principles between several other common decision tree algorithms, which first step towards performing a basic next employed actual case help us better understand Specifically, vehicle customer satisfaction data compare multiple algorithms cited some factors that affect corresponding countermeasures more conducive obtaining accurate results. results showed analyze very well reliably detect significantly correlated factors. This presents information required thereby enabling choices when use warranted.