Chi-squared Automatic Interaction Detection (CHAID)

CHAID is a popular Decision Tree Algorithm used for predictive modeling, especially in Data Mining and statistics.

This algorithm employs a hierarchical approach to recursively partition the data into mutually exclusive, non-overlapping homogeneous groups based on the significance of the predictor variables. CHAID uses the chi-squared test to determine potential interactions between variables at each level, which helps in creating a decision tree that maximizes predictive power.

One notable feature of CHAID is its ability to handle both Categorical Variables and continuous predictor variables, making it a versatile tool for exploring complex interactions within datasets. This makes CHAID particularly useful in fields such as market research, social sciences, and healthcare, where analyzing complex relationships between variables is essential.


  • CHAID differs from C4.5 and C5.0 in its approach, as it focuses on detecting interactions between variables while growing the tree.
  • The CHAID algorithm is often used in Exploratory Data Analysis (EDA) and has been effective in uncovering important relationships and interactions in various research fields.