Technical GlossaryData Science and Data Management

Threshold Moving

An approach that adjusts the classification threshold according to business goals and error costs in imbalanced settings.

Threshold moving changes how model probabilities are converted into hard decisions. The default 0.5 threshold is often far from optimal in imbalanced data problems. By using lower or higher thresholds, one can rebalance recall, precision, or business cost trade-offs. For that reason, threshold selection is not merely a technical optimization issue, but a decision-risk design problem. In practice, the right threshold often creates more value than the most accurate model alone.