misclassification

Misclassification is the assignment of an item to an incorrect category or class. In statistics, data science, and related fields, it describes a situation where the observed label does not match the true underlying category.

In machine learning, misclassification error is the rate at which a model’s predicted labels differ from the true labels. This error can be summarized in a confusion matrix, with false positives and false negatives as common forms of error. The overall performance of a classifier is often expressed as accuracy, the complement of misclassification rate.

Causes of misclassification include label noise from human error during data labeling, ambiguous or overlapping classes, biased or imbalanced datasets, measurement error, and changes over time such as concept drift. Misclassification can occur during data collection, annotation, or prediction, and may arise from limitations in the data or the model.

Implications vary by domain. In healthcare, misclassification can lead to incorrect diagnoses or inappropriate treatment. In finance or safety-critical settings, it can affect risk assessment and decision making. Reducing misclassification involves improving labeling protocols, using multiple annotators with adjudication, auditing data quality, and applying modeling approaches that account for uncertainty, such as probability calibration, threshold adjustment, and cost-sensitive learning.

Related concepts include the confusion matrix, precision, recall, and F1 score. The study of misclassification addresses the balance between error types, model complexity, data quality, and fairness, aiming to minimize incorrect predictions without compromising reliability or equity.