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R-squared in Watson OpenScale quality metrics
Last updated: Jun 15, 2023
R-squared in Watson OpenScale quality metrics

The R-squared is the ratio of the total variance and the explained variance in Watson OpenScale. R-squared measures how well the regression predictions approximate the actual values. The higher the R-squared score, the better the model fits to the actual values.

R-squared at a glance

  • Description: Ratio of difference between target variance and variance for prediction error to target variance
  • Default thresholds: Lower limit = 80%
  • Default recommendation:
    • Upward trend: An upward trend indicates that the metric is improving. This means that model retraining is effective.
    • Downward trend: A downward trend indicates that the metric is deteriorating. Feedback data is becoming significantly different than the training data.
    • 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 R-squared metric is defined in the following formula.

                  explained variation
R-squared =       _____________________

                    total variation

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Reviewing quality results

Parent topic: Quality metrics overview

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