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