False positive rate (FPR)

The false positive rate gives the proportion of incorrect predictions in positive class.

False positive rate (FPR)

  • Description: Proportion of incorrect predictions in positive class
  • Default thresholds: Lower limit = 80%
  • Default recommendation:
    • Upward trend: An upward trend indicates that the metric is deteriorating. Feedback data is becoming significantly different than the training data.
    • Downward trend: A downward trend indicates that the metric is improving. This means that model retraining is effective.
    • Erratic or irregular variation: An erratic or irregular variation indicates that the feedback data is not consistent between evaluations. Increase the minimum sample size for the Quality monitor.
  • Problem type: Binary classification
  • Chart values: Last value in the time frame
  • Metrics details available: Confusion matrix

Do the math

The The false positive rate is calculated as the total number of false positives divided by the number of false positives and the number of true negatives.

                        number of false positives
False positive rate =  ______________________________________________________

                       (number of false positives + number of true negatives)