Mean squared error

Mean squared error gives the mean of squared difference between model prediction and target value. It can be used as the measure of the quality of an estimator.

Mean squared error at a glance

• Description: Mean of squared 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 squared error in its simplest form is represented by the following formual.

                         SUM  (Yi - ^Yi) * (Yi - ^Yi)
Mean squared errors  =  ____________________________

                             number of errors