Structured Prediction
Structured prediction is a machine learning task where the output is a structured object, such as a sequence, tree, or graph, rather than a single label or scalar value. It involves predicting complex, interdependent variables that have inherent relationships, commonly used in natural language processing, computer vision, and bioinformatics. Techniques include conditional random fields, structured support vector machines, and neural sequence models.
Developers should learn structured prediction when working on tasks requiring predictions of interrelated outputs, such as part-of-speech tagging, named entity recognition, image segmentation, or protein structure prediction. It is essential for applications where output components depend on each other, improving accuracy over independent predictions by modeling these dependencies explicitly.