Node purity measures the homogeneity of the target variable within a node of a decision tree. A node is considered completely pure if all training observations in that node belong to a single class, representing perfect classification. A mixed node contains a combination of different classes. Split-selection algorithms (such as those using Gini index or entropy) aim to maximize node purity after a split is applied.