Regression

Report. To run a Preset on your data for a single current dataset:

report = Report([
    RegressionPreset(),
])

my_eval = report.run(current, None)

Test Suite. To add pass/fail regression quality Tests, auto-generated from the ref dataset:

report = Report([
    RegressionPreset(),
],
include_tests=True)

my_eval = report.run(current, ref)

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Overview

The RegressionPreset allows you to evaluate and visualize the performance on regression tasks. You can run this Report either for a single dataset or compare it against a reference dataset (such as past performance, or a different model/prompt).

The Report includes:

• Various metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), etc.

• Various visualizations: Actual vs Predicted Plot, Error Distribution, Error Normality, etc.

Additionally, if you include feature columns, the Report will show Regression Quality by groups with the highest errors (over- and under-prediction). It displays the relationship between columns/features and the target, showing how the system performs on different data segments.

Test Suite. If you enable Tests, this will automatically run checks to assess if the model performance metrics are within bounds.

Tests are auto-generated:

• Based on reference dataset. If the reference dataset is provided, conditions like expected prediction accuracy will be derived from it.

Based on heuristics. If there is no reference, Evidently will create a dummy regression model as a baseline and run checks against it.

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Use case

These Presets are useful in various scenarios:

• Model / system comparison. Compare predictive system performance across different datasets, such as during A/B testing, when experimenting with model configurations and architectures, etc.

• Production monitoring. You can run evaluations whenever you get actual values in production. Use this to communicate and visualize performance, decide on model updates / retraining, etc.

• Debugging. If you notice a drop in performance, use the visual Report to check error distributions and explore model errors.

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Data requirements

• Target and prediction columns. Required to calculate performance.

• One or two datasets. Pass two for a side-by-side comparison or to auto-generate tests.

• (Optional) Input features. Include if you want to explore underperforming segments.

• (Optional) Timestamp. If available, pass it to appear on some plots.

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Report Customization

You can customize the Report in several ways:

• Change Test conditions. To modify the auto-generated conditions, you can set yours: either a different condition relative to the reference or any custom conditions.

• Modify Report composition. You can add additional metrics, such as column Correlations, Missing Values, or Data Drift. It’s often useful to add ValueDrift("target") to evaluate if there is a statistical distribution shift in the model target (concept drift).