 Boosting a simple learner results in an unstructured set of formula_1 hypotheses, making it difficult to infer correlations between attributes.Alternating decision trees introduce structure to the set of hypotheses by requiring that they build off a hypothesis that was produced in an earlier iteration.The resulting set of hypotheses can be visualized in a tree based on the relationship between a hypothesis and its "parent."