Root of mean squared error in Watson OpenScale quality metrics

Last updated: Jun 15, 2023

The root-mean-squared error (RMSE) view shows the difference between the predicted and observed values in your Watson OpenScale model.

Root of mean squared error at a glance

Description: Square root of 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

The root of the mean-squared error is equal to the square root of the mean of (forecasts minus observed values) squared.

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RMSE  =  √(forecasts - observed values)*(forecasts - observed values)

Parent topic: Quality metrics overview