Structured prediction or structured (output) learning is an umbrella term for supervised machine learning techniques that involve predicting structured objects, rather than scalar discrete or real values.For example, the problem of translating a natural language sentence into a syntactic representation such as a parse tree can be seen as a structured prediction problem in which the structured output domain is the set of all possible parse trees.Probabilistic graphical models form a large class of structured prediction models.