In data science, the false positive rate measures the percentage of false positives against all positive predictions (the sum of false positives and true positives) in a binary classification problem. The false positive rate is based on how many actual negatives the model predicted incorrectly. This metric is complementary to the true positive rate, or recall, which shows how many actual positives the model predicted correctly.
False positive rate is one of several ways to measure the performance of machine learning models applied to classification problems. Other measures include precision, recall, accuracy, and F1 score. False positive rate is important when the cost of incorrectly identifying a positive is high, creating additional work or expense.
The C3 AI Platform provides a rich library of performance metrics to data scientists as they train and evaluate predictive models for classification problems. Data scientists and developers can easily view the performance of their models using all the different perspectives in a visual manner to experiment with tuning parameters to get the desired level of predictive outcomes. [Link to Varun’s blog post: /blog/a-visual-guide-to-binary-classification-metrics/]