Classification Threshold

Many classification models generate probabilities rather than directly assigning categories. A classification threshold determines when a prediction belongs to a particular class. For example, with a threshold of 0.5, predictions above that value may be classified as positive, while those below it are classified as negative. Adjusting the threshold changes the balance between false positives and false negatives.

The appropriate threshold depends on the consequences of different prediction errors. Changing it can affect precision, recall, sensitivity, and other performance metrics. Selecting the right threshold helps organizations align model behavior with the risks and requirements of a specific application.