Conditional random fields

Conditional Random Fields (CRFs) are statistical models used for predicting sequences or structures, such as labeling words in a sentence or identifying parts of an image. They work by considering the relationships between neighboring elements, using context to make more accurate predictions. Unlike models that look at each element in isolation, CRFs analyze the entire sequence to ensure consistency and capture dependencies, making them useful in natural language processing, bioinformatics, and computer vision tasks where understanding context and structure is important.