Classification and regression trees are methods that deliver models that meet both explanatory and predictive goals. Two of the strengths of this method are on the one hand the simple graphical representation by trees, and on the other hand the compact format of the natural language rules. We distinguish the following two cases where these modeling techniques should be used:
- use classification trees to explain and predict the belonging of objects (observations, individuals) to a class, on the basis of explanatory quantitative and qualitative variables.
- use regression tree to build an explanatory and predicting model for a dependent quantitative variable based on explanatory quantitative and qualitative variables.
XLSTAT uses the CHAID, exhaustive CHAID, QUEST and C&RT (Classification and Regression Trees) algorithms.