Conditional Random Field

A Conditional Random Field (CRF) is a statistical model used to predict sequences of labels for data, such as identifying parts of speech in a sentence or labeling pixels in an image. It considers the relationships between neighboring labels, ensuring that predictions are consistent and contextually appropriate. Unlike models that treat each part independently, CRFs leverage the context provided by surrounding information, leading to more accurate and coherent results. Essentially, CRFs analyze patterns and dependencies within data to produce more reliable and connected labelings.