Also known as: FPR
False Positive Rate (FPR) is the proportion of actual negative examples that a model incorrectly predicts as positive. It is commonly used in binary classification evaluation and forms the x-axis of the ROC curve.
The proportion of actual negative examples for which the model mistakenly predicted the positive class. The following formula calculates the false positive rate: The false positive rate is the x-axis in an ROC curve. See Classification: ROC and AUC in Machine Learning Crash Course for more information.
A side-by-side comparison of False Positive Rate and False Negative Rate. It explains how wrongly flagging actual negatives differs from missing actual positives in classification evaluation.
A side-by-side comparison of True Positive Rate and False Positive Rate. Understand how detection of actual positives differs from mistaken positive predictions among actual negatives.