Mispredicted

Mispredicted is an adjective used to describe an outcome, value, or class that was forecast, predicted, or expected by a model, forecast, or judgment but did not occur as predicted. The term covers both overprediction (the predicted value exceeds the actual value) and underprediction (the actual value exceeds the predicted). It is commonly used in statistics, forecasting, and data science to characterize errors in prediction.

In statistical modeling, mispredictions are part of prediction error and can be evaluated with metrics such as mean absolute error, mean squared error, or calibration error. In classification tasks, a mispredicted instance is one whose predicted label differs from the true label, contributing to the misclassification rate.

Mispredictions arise from data issues (noisy or biased data), model issues (misspecification, insufficient capacity), and real-world changes (concept drift, distribution shift). They can be amplified by outliers, nonlinear relationships, or temporally changing patterns.

Mitigation involves improving data quality, using robust or probabilistic models, cross-validation, regularization, ensembling, and updating models as new data arrive. Uncertainty quantification and predictive intervals help convey the range of plausible outcomes, reducing overreliance on a single predicted value.