Mean absolute error
Mean absolute error gives the mean of absolute difference between model prediction and target value.
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 time frame
- Metrics details available: None
Do the math
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