Weighted False Positive Rate in Watson OpenScale quality metrics

The Weighted False Positive Rate gives the weighted mean of class false positive rate (FPR) with weights equal to class probability in Watson OpenScale.

Weighted False Positive Rate (wFPR) 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: Multiclass classification
Chart values: Last value in the timeframe
Metrics details available: Confusion matrix

Do the math

The Weighted False Positive Rate is the application of the FPR with weighted data.

                   number of false positives
FPR =  ______________________________________________________

       (number of false positives + number of true negatives)

Learn more

Reviewing quality results

Parent topic: Quality metrics overview