Conditional classification trees by weighting the Gini impurity measure

This paper introduces the concept of the conditional impurity in the framework of tree-based models in order to deal with the analysis of three-way data, where a response variable and a set of predictors are measured on a sample of objects in different occasions. The conditional impurity in the definition of splitting criterion is defined as a classical impurity measure weighted by a predictability index