misclassified

Misclassified describes something that has been assigned to the wrong category, label, or class. The term is used across disciplines, from data analysis to biology and law, to indicate incorrect categorization that can bias conclusions or outcomes.

In data science and machine learning, a misclassified instance is one for which the predicted label does not match the true or reference label. The proportion of such instances is the misclassification rate, a key component of model evaluation alongside accuracy, precision, recall, and the confusion matrix. In taxonomy and biology, organisms may be misclassified when they are placed in an incorrect genus, family, or species, a mistake corrected through taxonomic revision and genetic evidence.

In medicine and epidemiology, misclassification of disease status, exposure, or outcomes can distort study results and patient care. Misclassification bias occurs when errors are systematic rather than random. In legal and employment contexts, misclassification commonly refers to incorrectly labeling workers as employees or independent contractors, with consequences for wages, benefits, and taxation.

Causes of misclassification include ambiguous or noisy data, insufficient features, labeling errors, or model bias. Preventive measures include careful data curation, clear class definitions, reproducible labeling protocols, robust validation, and domain expertise to ensure that categories reflect real distinctions.