A. Decision tree models segment a dataset into distinct classes through a series of binary decisions based on input variables. Each split in the tree is based on metrics like entropy or the gini index to measure impurity, determining how well a split separates the data. The tree continues to split until it reaches leaf nodes, which represent the final prediction. Information gain, calculated using entropy, helps in selecting the best splits to improve prediction accuracy.