What is a CHAID analysis?

CHAID analysis, which stands for Chi-squared Automatic Interaction Detection, is fundamentally a decision-tree methodology that focuses on identifying predictive variables. It utilizes Chi-square testing to evaluate the relationship between categorical variables, allowing the model to split the data into distinct groups based on these statistical relationships. This approach enables the identification of significant interactions among variables, leading to a tree structure that can be used for classification and predictive modeling.

The focus on Chi-square testing is crucial because it allows CHAID to assess the strength of associations between variables effectively, making it particularly well-suited for categorical data. The decision-tree created through CHAID actually guides analysts in understanding how different predictors interact to impact outcomes, which is paramount in predictive modeling situations.

By employing this method, analysts can derive insights into which combination of variables is most predictive of a response, making CHAID analysis a valuable tool in the toolkit of predictive analytics.