Mean-squared error in Watson OpenScale quality metrics

Last updated: Jun 15, 2023

Mean-squared error gives the mean of squared difference between model predictions and target values in Watson OpenScale. 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 timeframe
Metrics details available: None

Do the math

The Mean squared error in its simplest form is represented by the following formula.

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

                             number of errors