thresholdfocused
Thresholdfocused is a term used in statistics and machine learning to describe an approach that prioritizes decision thresholds of a probabilistic classifier rather than optimizing global accuracy or ranking metrics. The focus is on the point at which a model's predicted probabilities are converted into binary decisions, aligning the operating point with domain-specific objectives.
In practice thresholdfocused methods are used when false positives and false negatives carry different costs or
Common techniques include analyzing receiver operating characteristic or precision-recall curves to identify thresholds at points of
Applications include medical diagnosis, fraud detection, spam filtering, credit scoring, and quality control, where decisions hinge
Advantages of thresholdfocused approaches include improved alignment with real-world objectives and enhanced interpretability of decisions. Limitations
Related concepts include thresholding, ROC analysis, probability calibration, and cost-sensitive learning.