Threshold

The threshold is the critical boundary value in the decision-making mechanism of machine learning algorithms, particularly binary classifiers and activation functions, which divides the continuous probabilistic variables computed by the model into discrete categories or classes (e.g., yes/no, 0/1). During the process, the system compares the generated confidence value against this predefined level. If the result exceeds the threshold, activation or positive class assignment occurs; otherwise, the input is discarded. Dynamic optimization of the threshold (e.g., balancing precision and recall) is of fundamental importance for minimizing false positives and false negatives, directly affecting the reliability and sensitivity of the application — for example, a medical diagnostic system or a cybersecurity spam filter.