Summary Tests

Drift

Test for differences in the distribution of the reference dataset versus the evaluation dataset. If predictions and labels are provided, measure the performance degradation caused by shifting data as well as drift in predictions and labels themselves.

Labels and predictions are not required but they improve results.

Abnormal Inputs

Check the evaluation dataset for abnormal values commonly encountered in production. If model predictions are provided, test if the observed abnormal values cause a degradation in your model’s performance.

Labels and predictions are not required but they improve results.

Subset Performance

Test that your model performs equally well across different subsets of the evaluation dataset.

Predictions are required. Labels are required for most tests.

Bias And Fairness

Test that your model does not discriminate based on protected features.

For some tests predictions, labels, and/or model are required.

Transformations

Augment your evaluation dataset with synthetic abnormal values to proactively test your pipeline’s error-handling behavior and measure the performance degradation caused by different types of abnormal values.

Model is required. Labels are not required but they improve results.

Model Performance

Test that your model performs well on the evaluation dataset and is not degrading in performance.

Predictions are required. Labels are required for exact performance metrics, otherwise approximate performance metrics will be used.

Data Cleanliness

Test for data reliability by checking that your data is consistent and complete.

Attacks

Test the robustness of your model by measuring the maximum difference in model predictions that can be caused by small perturbations to data points.

Model is required.

Finance Compliance

Test that your model aligns with financial regulatory standards. This test suite consists of a selection of bias and fairness, abnormal input, and attack tests.

For some tests predictions, labels, and/or model are required.

Hiring Compliance

Test that your model aligns with hiring regulatory standards. This test suite consists of a selection of bias and fairness tests.

For some tests predictions, labels, and/or model are required.

Healthcare Compliance

Test that your model aligns with healthcare regulatory standards. This test suite consists of a selection of bias and fairness, data cleanliness, and data leakage tests.

For some tests predictions, labels, and/or model are required.

Subset Performance Degradation

Test that your model’s performance on different subsets of the data has not degraded.

Predictions are required. Labels are required for most tests.