Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account.
Conditional random fields (CRFs) are a class of statistical modeling methods frequently used in pattern recognition and machine learning, particularly for structured prediction.  A classifier predicts a label for a single sample without considering neighboring samples, whereas a CRF can incorporate context from surrounding samples.