Fitting models from noisy heuristic labels
Read OriginalThis technical article details the 'data programming' paradigm, a weak supervision method that uses maximum likelihood estimation to generate soft labels from heuristic labeling functions. It explains how to train binary classification models without true labels, covering the underlying probability model, log-likelihood estimation, and practical implementation, referencing the Snorkel package.
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