False Positive Rate

False Positive Rate is a measure of the accuracy of a machine learning model in predicting positive outcomes. It is the proportion of instances where the model predicted a positive outcome but the actual outcome was negative.

False positive rate is an important metric to consider when developing and evaluating machine learning models, especially in situations where the consequences of false positive predictions are severe. For example, if a model is used to predict fraudulent activity in a financial system, false positive predictions may result in innocent individuals being wrongly accused of fraud. In this case, it is important to minimize the false positive rate to avoid negative consequences for innocent people.