Node purity is the homogeneity of class labels among the training observations in a classification-tree node. A node is pure when all observations in it belong to one class; a mixed node contains observations from multiple classes. Split-selection criteria such as the Gini index and entropy favor splits that reduce impurity, thereby producing purer child nodes.