Mean-absolute error in Watson OpenScale quality metrics

The mean-absolute error gives the mean absolute difference between model predictions and target values in Watson OpenScale.

Mean-absolute error at a glance

Description: Mean of absolute difference between model prediction and target value
Default thresholds: Upper 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: Regression
Chart values: Last value in the timeframe
Metrics details available: None

The Mean absolute error is calculated by adding up all the absolute errors and dividing them by the number of errors.

                         SUM  | Yi - Xi | 
Mean absolute errors =  ____________________

                          number of errors

Parent topic: Quality metrics overview