What is adaptive thresholding in classification systems?

Adaptive thresholding dynamically adjusts classification decision thresholds based on context or data distribution.

Instead of using a fixed threshold (e.g., 0.5), adaptive systems adjust thresholds based on class imbalance, time, user segment, or risk level. This is common in fraud detection where false positives and false negatives have different costs. Thresholds can be learned using validation optimization or reinforcement learning strategies.