False positive rate in Watson OpenScale quality metrics

The false positive rate gives the proportion of incorrect predictions for positive classes in Watson OpenScale.

False positive rate at a glance

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 timeframe
Metrics details available: Confusion matrix

Do the math

The false positive rate is quotient of the total number of false positives that is divided by the sum of false positives and true negatives.

                        number of false positives
False positive rate =  ______________________________________________________

                       (number of false positives + number of true negatives)

Learn more

Reviewing quality results

Parent topic: Quality metrics overview