Порог классификации

In a binary classification, a number between 0 and 1 that converts the raw output of a logistic regression model into a prediction of either the positive class or the negative class. Note that the classification threshold is a value that a human chooses, not a value chosen by model training. A logistic regression model outputs a raw value between 0 and 1. Then: - If this raw value is greater than the classification threshold, then the positive class is predicted. - If this raw value is less than the classification threshold, then the negative class is predicted. For example, suppose the classification threshold is 0.8. If the raw value is 0.9, then the model predicts the positive class. If the raw value is 0.7, then the model predicts the negative class. The choice of classification threshold strongly influences the number of false positives and false negatives. As models or datasets evolve, engineers sometimes also change the classification threshold. When the classification threshold changes, positive class predictions can suddenly become negative classes and vice-versa. For example, consider a binary classification disease prediction model. Suppose that when the system runs in the first year: - The raw value for a particular patient is 0.95. - The classification threshold is 0.94. Therefore, the system diagnoses the positive class. (The patient gasps, "Oh no! I'm sick!") A year later, perhaps the values now look as follows: - The raw value for the same patient remains at 0.95. - The classification threshold changes to 0.97. Therefore, the system now reclassifies that patient as the negative class. ("Happy day! I'm not sick.") Same patient. Different diagnosis. See Thresholds and the confusion matrix in Machine Learning Crash Course for more information.