A classification tree applies the decision tree method to classification problems, predicting qualitative (categorical) rather than quantitative responses. It uses recursive binary splitting similar to a regression tree. However, instead of using Residual Sum of Squares (RSS), binary splits are evaluated using criteria like the Gini index or entropy, which measure node purity. The prediction for a given region is typically the most common class among the training observations in that terminal node.