Tests

Model Performance

Average Confidence

This test checks the average confidence of the model predictions between the reference and evaluation sets to see if the metric has experienced significant degradation. The "confidence" of a prediction for classification tasks is defined as the distance between the probability of the predicted class (defined as the argmax over the prediction vector) and 1. We average this metric across all predictions.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly. Since oftentimes labels are not available in a production setting, this metric can serve as a useful proxy for model performance.

Configuration: By default, this test runs if predictions are specified (no labels required).

Example: Assume that on the reference set the model obtained 0.85 average confidence but on the evaluation set without labels we predict that the model obtained 0.5 average confidence. Then this test raises a warning.

Average Thresholded Confidence

This test checks the average thresholded confidence (ATC) of the model predictions between the reference and evaluation sets to see if the metric has experienced significant degradation. ATC is a method for estimating accuracy of unlabeled examples taken from this paper. The threshold is first computed on the reference set: we pick a confidence threshold such that the percentage of datapoints whose max predicted probability is less than the threshold is around equal to the error rate of the model (here, it is 1-accuracy) on the reference set. Then, we apply this threshold in the evaluation set: the predicted accuracy is then equal to the percentage of datapoints with max predicted probability greater than this threshold.

Why it matters: During production, factors like distribution shift may cause model performance to decrease significantly. Since oftentimes labels are not available in a production setting, this metric can serve as a useful proxy for model performance.

Configuration: By default, this test runs if predictions/labels are specified in the reference set and predictions are specified in the eval set (no labels required).

Example: Assume that on the reference set the model obtained 0.85 accuracy but on the evaluation set, we find that only 55 percent of datapoints have max predicted probability greater than our threshold. Then our predicted accuracy is 0.55 and this test raises a warning.

Calibration Comparison

This test checks that the reference and evaluation sets have sufficiently similar calibration curves as measured by the Mean Squared Error (MSE) between the two curves. The calibration curve is a line plot where the x-axis represents the average predicted probability and the y-axis is the proportion of positive predictions. The curve of the ideal calibrated model is thus a linear straight line from (0, 0) moving linearly.

Why it matters: Knowing how well-calibrated your model is can help you better interpret and act upon model outputs, and can even be an indicator of generalization. A greater difference between reference and evaluation curves could indicate a lack of generalizability. In addition, a change in calibration could indicate that decision-making or thresholding conducted upstream needs to change as it is behaving differently on held-out data.

Configuration: By default, this test runs over the predictions and labels.

Example: Suppose the model’s task is binary classification and predicts whether or not a data point is fraudulent. If we have a reference set in which 1% of the data points are fraudulent, but an evaluation set where 50% are fraudulent, then our model may not be well calibrated, and the MSE difference in the curves will be large, resulting in a failing test.

AUC

This test checks the AUC metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of AUC has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the AUC metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Prediction Variance (Negative Labels)

This test checks the Prediction Variance (Negative Labels) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Prediction Variance (Negative Labels) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Prediction Variance (Negative Labels) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Multiclass AUC

This test checks the Multiclass AUC metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Multiclass AUC has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Multiclass AUC metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Mean Reciprocal Rank (MRR)

This test checks the Mean Reciprocal Rank (MRR) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Mean Reciprocal Rank (MRR) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Mean Reciprocal Rank (MRR) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Mean-Absolute Percentage Error (MAPE)

This test checks the Mean-Absolute Percentage Error (MAPE) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Mean-Absolute Percentage Error (MAPE) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Mean-Absolute Percentage Error (MAPE) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Root-Mean-Squared Error (RMSE)

This test checks the Root-Mean-Squared Error (RMSE) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Root-Mean-Squared Error (RMSE) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Root-Mean-Squared Error (RMSE) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Macro Precision

This test checks the Macro Precision metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Macro Precision has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Macro Precision metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Accuracy

This test checks the Accuracy metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Accuracy has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Accuracy metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Recall

This test checks the Recall metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Recall has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Recall metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Normalized Discounted Cumulative Gain (NDCG)

This test checks the Normalized Discounted Cumulative Gain (NDCG) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Normalized Discounted Cumulative Gain (NDCG) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Normalized Discounted Cumulative Gain (NDCG) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Prediction Variance

This test checks the Prediction Variance metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Prediction Variance has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Prediction Variance metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Precision

This test checks the Precision metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Precision has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Precision metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

False Negative Rate

This test checks the False Negative Rate metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of False Negative Rate has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the False Negative Rate metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Mean-Squared-Log Error (MSLE)

This test checks the Mean-Squared-Log Error (MSLE) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Mean-Squared-Log Error (MSLE) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Mean-Squared-Log Error (MSLE) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Multiclass Accuracy

This test checks the Multiclass Accuracy metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Multiclass Accuracy has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Multiclass Accuracy metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Rank Correlation

This test checks the Rank Correlation metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Rank Correlation has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Rank Correlation metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Mean-Absolute Error (MAE)

This test checks the Mean-Absolute Error (MAE) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Mean-Absolute Error (MAE) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Mean-Absolute Error (MAE) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Macro Recall

This test checks the Macro Recall metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Macro Recall has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Macro Recall metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Mean-Squared Error (MSE)

This test checks the Mean-Squared Error (MSE) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Mean-Squared Error (MSE) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Mean-Squared Error (MSE) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Macro F1

This test checks the Macro F1 metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Macro F1 has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Macro F1 metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

F1

This test checks the F1 metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of F1 has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the F1 metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Average Prediction

This test checks the Average Prediction metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Average Prediction has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Average Prediction metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Prediction Variance (Positive Labels)

This test checks the Prediction Variance (Positive Labels) metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Prediction Variance (Positive Labels) has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Prediction Variance (Positive Labels) metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

False Positive Rate

This test checks the False Positive Rate metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of False Positive Rate has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the False Positive Rate metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Average Rank

This test checks the Average Rank metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Average Rank has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Average Rank metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Positive Prediction Rate

This test checks the Positive Prediction Rate metric to see both if its performance on the evaluation set alone is satisfactory, as well as if performance in terms of Positive Prediction Rate has degraded from the reference to evaluation set. The key detail displays whether the given performance metric has degraded beyond a defined threshold.

Why it matters: During production, factors like distribution shift or a change in p(y|x) may cause model performance to decrease significantly.

Configuration: By default, this test runs over the Positive Prediction Rate metric with the below thresholds set for the absolute and degradation tests.

Example: Assume that on the reference set the model obtained 0.85 AUC but on the evaluation set the model obtained 0.5 AUC. Then this test raises a warning.

Bias and Fairness

Protected Feature Drift

This test measures the severity of passing to the model data points that have categorical features which have drifted from the distribution observed in the reference set. The severity is a function of the impact on the model, as well as the presence of drift in the data. The model impact measures how much model performance changes due to drift in the given feature. The key detail displayed is the PSI test statistic, which is a measure of how statistically significant the difference between the frequencies of categorical values in the reference and evaluation sets is.

Why it matters: Distribution drift in categorical features between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the preprocessing pipeline. A big shift in categorical features towards categorical subsets that your model performs poorly in could indicate a degradation in model performance and signal the need for relabeling and retraining.

Configuration: By default, this test runs over all categorical columns with sufficiently many samples.

Example: Suppose that the observed frequencies of the isLoggedIn feature is [100, 200] in the reference set but [25, 150] in the test set. Then the PSI would be 0.201. If our PSI threshold was 0.1 then the test would fail.

Demographic Parity (Pos Pred)

This test checks whether the Demographic Parity for any subset of a feature performs as well as the best Demographic Parity across all subsets of that feature. The Demographic Parity is calculated as the Positive Prediction Rate. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Demographic Parity of model predictions within a specific subset is significantly lower than that of other subsets by taking a ratio of the rates.

Why it matters: Assessing differences in Demographic Parity is an important measures of fairness. It is meant to be used in a setting where we assert that the base Demographic Paritys between subgroups should be the same (even if empirically they are different). This contrasts with equality of opportunity or predictive parity tests, which permit classification rates to depend on a sensitive attribute. It can be useful in legal/compliance settings where we want a Demographic Parity for any sensitive group to fundamentally be the same as other groups.

Configuration: By default, the Demographic Parity is computed for all protected features. The severity threshold baseline is set to 80% by default, in accordance with the four-fifths law for adverse impact detection.

Example: Suppose we had data with the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog'], and model predictions [0.3, 0.3, 0.9, 0.9, 0.9, 0.3]. Then regardless of the labels, the Positive Prediction Rate over the feature values ('cat', 'dog') would be (0.33, 0.66), indicating a failure because cats would be selected half as often as dogs.

Demographic Parity (Avg Pred)

This test checks whether the Average Prediction for any subset of a feature performs as well as the best Average Prediction across all subsets of that feature. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Average Prediction of model predictions within a specific subset is significantly lower than that of other subsets by taking a ratio of the rates.

Why it matters: Assessing differences in Average Prediction is an important measures of fairness. It is meant to be used in a setting where we assert that the base Average Predictions between subgroups should be the same (even if empirically they are different). This contrasts with equality of opportunity or predictive parity tests, which permit classification rates to depend on a sensitive attribute. It can be useful in legal/compliance settings where we want a Average Prediction for any sensitive group to fundamentally be the same as other groups.

Configuration: By default, the Average Prediction is computed for all protected features. The severity threshold baseline is set to 80% by default, in accordance with the four-fifths law for adverse impact detection.

Example: Suppose we had data with the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog'], and model predictions [10.4, 10.0, 10.2, 7.7, 8.0, 8.0]. Then regardless of the labels, the Positive Prediction Rate over the feature values ('cat', 'dog') would be (10.2, 7.9), indicating a failure because dogs have an Average Prediction less than 80% of the Average Prediction for cats.

Demographic Parity (Avg Rank)

This test checks whether the Average Rank for any subset of a feature performs as well as the best Average Rank across all subsets of that feature. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Average Rank of model predictions within a specific subset is significantly lower than that of other subsets by taking a ratio of the rates.

Why it matters: Assessing differences in Average Rank is an important measures of fairness. It is meant to be used in a setting where we assert that the base Average Ranks between subgroups should be the same (even if empirically they are different). This contrasts with equality of opportunity or predictive parity tests, which permit classification rates to depend on a sensitive attribute. It can be useful in legal/compliance settings where we want a Average Rank for any sensitive group to fundamentally be the same as other groups.

Configuration: By default, the Average Rank is computed for all protected features. The severity threshold baseline is set to 80% by default, in accordance with the four-fifths law for adverse impact detection.

Example: Suppose we had data with the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog'], and model predictions [0.3, 0.4, 0.5, 0.7, 0.8, 0.9], and rank [6, 5, 4, 3, 2, 1]. Then regardless of the labels, the Average Rank over the feature values ('cat', 'dog') would be (5, 2), indicating a failure in Average Rank.

Class Imbalance

This test checks whether the training sample size for any subset of a feature is significantly smaller than other subsets of that feature. The test first splits the dataset into various subset classes within the feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the class imbalance measure of that subset compared to the largest subset exceeds a set threshold.

Why it matters: Assessing class imbalance is an important measure of fairness. Features with low subset sizes can result in the model overfitting those subsets, and hence cause a larger error when those subsets appear in test data. This test can be useful in legal/compliance settings where sufficient data for all subsets of a protected feature is important.

Configuration: By default, class imbalance is tested for all protected features.

Example: Suppose we had data with the protected feature 'animal', where the distribution of the feature over subsets was 80% dog, 19% cat, and 1% rabbit. The class imbalance ratio hence would be 0.616 for cat and 0.975 for rabbit. The CI ratio for rabbit is close to the extreme of 1, implying that a model trained on this data might perform worse when making predictions on rabbits than over the other subsets.

Equalized Odds

This test checks for equal true positive and false positive rates over all subsets for each protected feature. The test first splits the dataset into various subset classes within the feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the true positive and false positive rates of that subset significantly varies as compared to the largest subset.

Why it matters: Equalized odds (or disparate mistreatment) is an important measure of fairness in machine learning. Subjects in protected groups may have different true positive rates or false positive rates, which imply that the model may be biased on those protected features. Fulfilling the condition of equalized odds may be a requirement in various legal/compliance settings.

Configuration: By default, equalized odds is tested for all protected features.

Example: Suppose we had data with the protected feature 'animal', where the true positive rates over the subsets 'dog', 'cat', and 'rabbit' were [0.6, 0.9, 0.1], and the false positive rates over the same subsets were [0.3, 0.33, 0.31]. Equalized odds tests for consistency over all true positive prediction rates and false positive prediction rates, hence this would result in a test failure because there is high discrepancy in the true positive rates over the subsets.

Feature Independence

This test checks the independence of each protected feature with the predicted label class. It runs over categorical protected features and uses the chi square test of independence to determine the feature independence. The test compares the observed data to a model that distributes the data according to the expectation that the variables are independent. Wherever the observed data does not fit the model, the likelihood that the variables are dependent becomes stronger.

Why it matters: A test of independence assesses whether observations consisting of measures on two variables, expressed in a contingency table, are independent of each other. This can be useful when assessing how protected features impact the predicted class and helping with the feature selection process.

Configuration: By default, this test is run over all protected categorical features.

Example: Let's say you have a model that predicts whether or not a person will be hired or not. One protected feature is gender. If these two variables are independent then the male-female ratio across hired and not hired should be the same. The p-value is 0.06 and the chi squared value is 300. The p-value is above the threshold of 0.05 to declare independence.

Predict Protected Features

The Predict Protected Features test works by training a multi-class logistic regression model to infer categorical protected features from unprotected categorical and numerical features. The model is fit to the reference data and scored based on its accuracy over the evaluation data. The unprotected categorical features are one-hot encoded.

Why it matters: In a compliance setting, it may be prohibited to include certain protected features in your training data. However, unprotected features might still provide your model with information about the protected features. If a simple logistic regression model can be trained to accurately predict protected features, your model might have a hidden reliance on protected features, resulting in biased decisions.

Configuration: By default, the selection rate is computed for all protected features.

Example: Suppose we had data with the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog'], and unprotected feature 'age': [15, 10, 16, 2, 3, 7]. Then if a logistic regression model is trained to predict 'animal' based on 'age', it might achieve a high accuracy, indicating that the unprotected feature 'age' could be used to easily predict the protected feature 'animal'

Equal Opportunity (Recall)

The recall test is more popularly referred to as equal opportunity or false negative error rate balance in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Recall of model predictions within a specific subset is significantly lower than the model prediction Recall over the entire population.

Why it matters: Having different true positive rates (e.g. equal opportunity) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. An intuitive example is when the label indicates a positive attribute: if predicting whether to interview a given candidate, make sure that out of qualified candidates, the rate at which the model predicts a rejection is similar to group A and B.

Configuration: By default, Recall is computed over all predictions/labels. Note that we round predictions to 0/1 to compute recall.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the Recall over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.66.

Equal Opportunity (Macro Recall)

The recall test is more popularly referred to as equal opportunity or false negative error rate balance in fairness literature. When transitioning to the multiclass setting we can use macro recall which computes the recall of each individual class and then averages these numbers.This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Macro Recall of model predictions within a specific subset is significantly lower than the model prediction Macro Recall over the entire population.

Why it matters: Having different true positive rates (e.g. equal opportunity) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. An intuitive example is when the label indicates a positive attribute: if predicting whether to interview a given candidate, make sure that out of qualified candidates, the rate at which the model predicts an interview is similar to group A and B.

Configuration: By default, Macro Recall is computed over all predictions/labels. Note that the predicted label is the label with the largest predicted class probability.

Example: Suppose we are differentiating between cats, bears, and dogs. Assume that across the data points where height=2 the predictions are [0.9, 0.1, 0], [0.1, 0.9, 0], [0.2, 0.1, 0.7] and the labels are [1, 0, 0], [1, 0, 0], [0, 0, 1] (where the first index corresponds to cat, the second corresponds to bear, and the third corresponds to dog). Then the Macro Recall across this subset is 0.67. If the overall Macro Recall across all subsets is 0.9 then this test raises a warning.

Intersectional Group Fairness (Pos Pred)

This test checks whether the model performs equally well across subgroups created from the intersection of protected groups. The test first creates unique pairs of categorical protected features. We then test whether the positive prediction rate of model predictions within a specific subset is significantly lower than the model positive prediction rate over the entire population. This will expose hidden biases against groups at the intersection of these protected features

Why it matters: Most existing work in the fairness literature deals with a binary view of fairness - either a particular group is performing worse or not. This binary categorization misses the important nuance of the fairness field - that biases can often be amplified in subgroups that combine membership from different protected groups, especially if such a subgroup is particularly underrepresented in opportunities historically. The intersectional group fairness test is run over subsets representing this intersection between two protected groups.

Configuration: This test runs over unique pairs of categorical protected features.

Example: Suppose your dataset contains two protected features: race and gender. Both features pass the demographic parity test for categories women, men, white and black. However, when certain subsets of these features are combined, such as black women or white men, the positive prediction rates perform significantly worse than the overall population. This would show disparate impact towards this subgroup.

Intersectional Group Fairness (Avg Pred)

This test checks whether the model performs equally well across subgroups created from the intersection of protected groups. The test first creates unique pairs of categorical protected features. We then test whether the average prediction of model predictions within a specific subset is significantly lower than the model average prediction over the entire population. This will expose hidden biases against groups at the intersection of these protected features

Why it matters: Most existing work in the fairness literature deals with a binary view of fairness - either a particular group is performing worse or not. This binary categorization misses the important nuance of the fairness field - that biases can often be amplified in subgroups that combine membership from different protected groups, especially if such a subgroup is particularly underrepresented in opportunities historically. The intersectional group fairness test is run over subsets representing this intersection between two protected groups.

Configuration: This test runs over unique pairs of categorical protected features.

Example: Suppose your dataset contains two protected features: race and gender. Both features pass the demographic parity test for categories women, men, white and black. However, when certain subsets of these features are combined, such as black women or white men, the positive prediction rates perform significantly worse than the overall population. This would show disparate impact towards this subgroup.

Intersectional Group Fairness (Avg Rank)

This test checks whether the model performs equally well across subgroups created from the intersection of protected groups. The test first creates unique pairs of categorical protected features. We then test whether the average rank of model predictions within a specific subset is significantly lower than the model average rank over the entire population. This will expose hidden biases against groups at the intersection of these protected features

Why it matters: Most existing work in the fairness literature deals with a binary view of fairness - either a particular group is performing worse or not. This binary categorization misses the important nuance of the fairness field - that biases can often be amplified in subgroups that combine membership from different protected groups, especially if such a subgroup is particularly underrepresented in opportunities historically. The intersectional group fairness test is run over subsets representing this intersection between two protected groups.

Configuration: This test runs over unique pairs of categorical protected features.

Example: Suppose your dataset contains two protected features: race and gender. Both features pass the demographic parity test for categories women, men, white and black. However, when certain subsets of these features are combined, such as black women or white men, the positive prediction rates perform significantly worse than the overall population. This would show disparate impact towards this subgroup.

Predictive Equality (FPR)

The false positive error rate test is also popularly referred to as as predictive equality, or equal mis-opportunity in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the false positive rate of model predictions within a specific subset is significantly higher than the model prediction false positive rate over the entire population.

Why it matters: Having different false positive rates (e.g. predictive equality) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. As an intuitive example, consider the case when the label indicates an undesirable attribute: if predicting whether a person will default on their loan, make sure that for people who didn't default, the rate at which the model incorrectly predicts positive is similar for group A and B.

Configuration: By default, false positive rate is computed over all predictions/labels. Note that we round predictions to 0/1 to compute false positive rate.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the false positive rate over the feature subset value 'cat' would be 1.0, compared to the overall metric of 0.67.

Discrimination By Proxy

This test checks whether any feature is a proxy for a protected feature. It runs over categorical features, using mutual information as a measure of similarity with a protected feature. Mutual information measures any dependencies between two variables.

Why it matters: A common strategy to try to ensure a model is not biased is to remove protected features from the training data entirely so the model cannot learn over them. However, if other features are highly dependent on those features, that could lead to the model effectively still training over those features by proxy.

Configuration: By default, this test is run over all categorical protected columns.

Example: Suppose we had data with a protected feature ('gender'). If there was another feature, like 'title', which was highly associated with 'gender', this test would raise a warning if the mutual information between those two features was particularly high.

Subset Sensitivity (Pos Pred)

This test measures how sensitive the model is to substituting the lowest performing subset of a feature into a sample of data. The test splits the dataset into various subsets based on the feature values and finds the lowest performing subset, based on the lowest Positive Prediction Rate. The test then substitutes this subset into a sample from the original data and calculates the change in Positive Prediction Rate. This test fails if a model demonstrates significantly lower Positive Prediction Rate on the lowest performing subset.

Why it matters: Assessing differences in model output is an important measure of fairness. If the model performs worse because of the value of a protected feature such as race or gender, then this could indicate bias. It can be useful in legal/compliance settings where we fundamentally want the prediction for any protected group to be the same as for other groups.

Configuration: By default, the subset sensitivity is computed for all protected features that are strings.

Example: Suppose the data had the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog', 'horse', 'horse'], and model predictions for cat were the lowest. If substituting cat for dog and horse in the other inputs causes model predictions to decrease, then this would indicate a failure because the model disadvantages cats.

Subset Sensitivity (Avg Pred)

This test measures how sensitive the model is to substituting the lowest performing subset of a feature into a sample of data. The test splits the dataset into various subsets based on the feature values and finds the lowest performing subset, based on the lowest Average Prediction. The test then substitutes this subset into a sample from the original data and calculates the change in Average Prediction. This test fails if a model demonstrates significantly lower Average Prediction on the lowest performing subset.

Why it matters: Assessing differences in model output is an important measure of fairness. If the model performs worse because of the value of a protected feature such as race or gender, then this could indicate bias. It can be useful in legal/compliance settings where we fundamentally want the prediction for any protected group to be the same as for other groups.

Configuration: By default, the subset sensitivity is computed for all protected features that are strings.

Example: Suppose the data had the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog', 'horse', 'horse'], and model predictions for cat were the lowest. If substituting cat for dog and horse in the other inputs causes model predictions to decrease, then this would indicate a failure because the model disadvantages cats.

Subset Sensitivity (Avg Rank)

This test measures how sensitive the model is to substituting the lowest performing subset of a feature into a sample of data. The test splits the dataset into various subsets based on the feature values and finds the lowest performing subset, based on the lowest Average Rank. The test then substitutes this subset into a sample from the original data and calculates the change in Average Rank. This test fails if a model demonstrates significantly lower Average Rank on the lowest performing subset.

Why it matters: Assessing differences in model output is an important measure of fairness. If the model performs worse because of the value of a protected feature such as race or gender, then this could indicate bias. It can be useful in legal/compliance settings where we fundamentally want the prediction for any protected group to be the same as for other groups.

Configuration: By default, the subset sensitivity is computed for all protected features that are strings.

Example: Suppose the data had the following protected feature 'animal': ['cat', 'cat', 'cat', 'dog', 'dog', 'dog', 'horse', 'horse'], and model predictions for cat were the lowest. If substituting cat for dog and horse in the other inputs causes model predictions to decrease, then this would indicate a failure because the model disadvantages cats.

Transformations

Out of Range Substitution

This test measures the impact on the model when we substitute values outside the inferred range of allowed values into clean datapoints.

Why it matters: In production, the model may encounter corrupted or manipulated out of range values. It is important that the model is robust to such extremities.

Configuration: By default, this test runs over all numeric features.

Example: In the reference set, the Age feature has a range of [0, 121]. This test raises a warning if substituting values outside of this range into Age (eg. 150, 200) causes model performance to decrease.

Numeric Outliers Substitution

This test measures the impact on the model when we substitute outliers into clean datapoints. Outliers are values which may not necessarily be outside of an allowed range for a feature, but are extreme values that are unusual and may be indicative of abnormality.

Why it matters: Outliers can be a sign of corrupted or otherwise erroneous data, and can degrade model performance if used in the training data, or lead to unexpected behaviour if input at inference time.

Configuration: By default this test is run over each numeric feature that is neither unique nor ascending.

Example: Suppose there is a feature age for which in the reference set the values 103 and 114 each appear once but every other value (with substantial sample size) is contained within the range [0, 97]. Then we would infer a lower outlier threshold of 0 and an upper outlier threshold of 97. This test raises a warning if substituting outliers into age causes model performance to decrease.

Int Feature Type Change

This test measures the impact on the model when we substitute values not of type Integer into features that are inferred to be Integer type from the reference set. In this specific test, we add a decimal value to the integer to convert it to a float.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Integer.

Example: Say that the feature X requires the Integer type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

Float Feature Type Change

This test measures the impact on the model when we substitute values not of type Float into features that are inferred to be Float type from the reference set. In this specific test, we cast the float as a string (2.3 becomes '2.3')

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Float.

Example: Say that the feature X requires the Float type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

String Feature Type Change

This test measures the impact on the model when we substitute values not of type String Categorical into features that are inferred to be String Categorical type from the reference set. In this specific test, we fix a random integer as the input.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type String Categorical.

Example: Say that the feature X requires the String Categorical type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

Boolean Feature Type Change

This test measures the impact on the model when we substitute values not of type Boolean Categorical into features that are inferred to be Boolean Categorical type from the reference set. In this specific test, we randomly fix an integer as the input.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Boolean Categorical.

Example: Say that the feature X requires the Boolean Categorical type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

URL Feature Type Change

This test measures the impact on the model when we substitute values not of type URL Categorical into features that are inferred to be URL Categorical type from the reference set. In this specific test, we create a random string and fix that as the input.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type URL Categorical.

Example: Say that the feature X requires the URL Categorical type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

Domain Feature Type Change

This test measures the impact on the model when we substitute values not of type Domain Categorical into features that are inferred to be Domain Categorical type from the reference set. In this specific test, we create a random string and fix that as the input.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Domain Categorical.

Example: Say that the feature X requires the Domain Categorical type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

Email Feature Type Change

This test measures the impact on the model when we substitute values not of type Email Categorical into features that are inferred to be Email Categorical type from the reference set. In this specific test, we create a random string and fix that as the input.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Email Categorical.

Example: Say that the feature X requires the Email Categorical type. This test raises a warning if changing values in X to a different type causes model performance to decrease.

Empty String Substitution

This test measures the impact on the model when we substitute empty string values instead of null values into clean datapoints.

Why it matters: In production, the model may encounter corrupted or manipulated string values. Null values and empty strings are often expected to be treated the same, but the model might not treat them that way. It is important that the model is robust to such extremities.

Configuration: By default, this test runs over all string features with null values.

Example: In the reference set, the Name feature contains nulls. This test raises a warning if substituting empty strings instead of null values into the Name feature causes model performance to decrease.

Required Characters Deletion

This test measures the impact on the model when we delete required characters, inferred from the reference set, from the strings of clean datapoints.

Why it matters: A feature may require specific characters. However, errors in the data pipeline may allow invalid data points that lack these required characters to pass. Failing to catch such errors may lead to noisier training data or noisier predictions during inference, which can degrade model metrics.

Configuration: By default, this test runs over all string features that are inferred to have required characters.

Example: Say that the feature email requires the character @. This test raises a warning if removing @ from values in email causes model performance to decrease

Unseen Categorical Substitution

This test measures the impact on the model when we substitute unseen categorical values into clean datapoints.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all categorical features.

Example: Say that the feature Animal contains the values ['Cat', 'Dog'] from the reference set. This test raises a warning if substituting unseen values into the feature Animal causes model performance to decrease.

Unseen Domain Substitution

This test measures the impact on the model when we substitute unseen domain values into clean datapoints.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all features inferred to contain domains.

Example: Say that the feature WebDomain contains the values ['gmail.com', 'hotmail.com'] from the reference set. This test raises a warning if substituting unseen values into the feature WebDomain causes model performance to decrease.

Unseen Email Substitution

This test measures the impact on the model when we substitute unseen email values into clean datapoints.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all features inferred to contain emails.

Example: Say that the feature Email contains the values ['[email protected]', '[email protected]'] from the reference set. This test raises a warning if substituting unseen values into the feature Email causes model performance to decrease.

Unseen URL Substitution

This test measures the impact on the model when we substitute unseen URL values into clean datapoints.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all features inferred to contain URLs.

Example: Say that the feature WebURL contains the values ['http://google.com', 'http://yahoo.com'] from the reference set. This test raises a warning if substituting unseen values into the feature WebURL causes model performance to decrease.

Null Substitution

This test measures the impact on the model when we substitute nulls in features that should not have nulls into clean datapoints.

Why it matters: The model may make certain assumptions about a column depending on whether or not it had nulls in the training data. If these assumptions break during production, this may damage the model's performance. For example, if a column was never null during training then a model may not have learned to be robust against noise in that column.

Configuration: By default, this test runs over all columns that had zero nulls in the reference set.

Example: Suppose that the feature Age was never null in the reference set. This test raises a warning if substituting nulls into the Age feature causes model performance to decrease.

Capitalization Change

This test measures the impact on the model when we substitute different types of capitalization into clean datapoints.

Why it matters: In production, models can come across the same value with different capitalizations, making it important to explicitly check that your model is invariant to such differences.

Configuration: By default, this test runs over all categorical features.

Example: Suppose we had a column that corresponded to country code. For a specific row, let's say the observed value in the reference set was USA. This test raises a warning if substituting different capitalizations of USA, eg.usa, causes model performance to decrease.

Drift

Correlation Drift (Feature-to-Feature)

This test measures the severity of feature-feature correlation drift from the reference to the evaluation set for a given pair of features. The severity is a function of the correlation drift in the data. The key detail is the difference in correlation scores between the reference and evaluation sets, along with an associated p-value. Correlation is a measure of the linear relationship between two numeric columns (feature-feature) so this test checks for significant changes in this relationship between each feature-feature in the reference and evaluation sets. To compute the p-value, we use Fisher's z-transformation to convert the distribution of sample correlations to a normal distribution, and then we run a standard two-sample test on two normal distributions.

Why it matters: Correlation drift between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the underlying processing stage. A big shift in these dependencies could indicate shifting datasets and degradation in model performance, signaling the need for relabeling and retraining.

Configuration: By default, this test runs over all pairs of features in the dataset.

Example: Suppose that the correlation between country and state is 0.5 in the reference set but 0.7 in the evaluation set, and the p-value is 0.03. Then the large difference in scores indicates that the dependency between the two features has drifted. If our difference threshold was 0.2, and p-value threshold was 0.05, then the test would fail.

Correlation Drift (Feature-to-Label)

This test measures the severity of feature-label correlation drift from the reference to the evaluation set for a given pair of a feature and label. The severity is a function of the correlation drift in the data. The key detail is the difference in correlation scores between the reference and evaluation sets, along with an associated p-value. Correlation is a measure of the linear relationship between two numeric columns (feature-label) so this test checks for significant changes in this relationship between each feature-label in the reference and evaluation sets. To compute the p-value, we use Fisher's z-transformation to convert the distribution of sample correlations to a normal distribution, and then we run a standard two-sample test on two normal distributions.

Why it matters: Correlation drift between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the underlying processing stage. A big shift in these dependencies could indicate shifting datasets and degradation in model performance, signaling the need for relabeling and retraining.

Configuration: By default, this test runs over all pairs of features and labels in the dataset.

Example: Suppose that the correlation between LotArea and SalePrice is 0.4 in the reference set but 0.8 in the evaluation set, and the p-value is 0.15. Then the large difference in scores indicates that the impact of the feature on the label has drifted. If our difference threshold was 0.2, and p-value threshold was 0.05, then the test would fail.

Mutual Information Drift (Feature-to-Feature)

This test measures the severity of feature mutual information drift from the reference to the evaluation set for a given pair of features. The severity is a function of the mutual information drift in the data. The key detail is the difference in mutual information scores between the reference and evaluation sets. Mutual information is a measure of how dependent two features are, so this checks for significant changes in dependence between pairs of features in the reference and evaluation sets.

Why it matters: Mutual information drift between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the underlying processing stage. A big shift in these dependencies could indicate shifting datasets and degradation in model performance, signaling the need for relabeling and retraining.

Configuration: By default, this test runs over all pairs of features in the dataset.

Example: Suppose that the mutual information between country and state is 0.5 in the reference set but 0.7 in the evaluation set. Then the large difference in scores indicates that the dependency between the two features has drifted. If our difference threshold was 0.2 then the test would fail.

Mutual Information Drift (Feature-to-Label)

This test measures the severity of feature mutual information drift from the reference to the evaluation set for a given pair of features. The severity is a function of the mutual information drift in the data. The key detail is the difference in mutual information scores between the reference and evaluation sets. Mutual information is a measure of how dependent two features are, so this checks for significant changes in dependence between pairs of features in the reference and evaluation sets.

Why it matters: Mutual information drift between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the underlying processing stage. A big shift in these dependencies could indicate shifting datasets and degradation in model performance, signaling the need for relabeling and retraining.

Configuration: By default, this test runs over all pairs of features in the dataset.

Example: Suppose that the mutual information between country and state is 0.5 in the reference set but 0.7 in the evaluation set. Then the large difference in scores indicates that the dependency between the two features has drifted. If our difference threshold was 0.2 then the test would fail.

Label Drift (Categorical)

This test checks that the difference in label distribution between the reference and evaluation sets is small, using PSI test. The key detail displayed is the PSI statistic which is a measure of how different the frequencies of the column in the reference and evaluation sets are.

Why it matters: Label distribution shift between reference and test can indicate that the underlying data distribution has changed significantly enough to modify model decisions. This may mean that the model needs to be retrained to adjust to the new data environment. In addition, significant label distribution shift may indicate that upstream decision-making modules (e.g. thresholds) may need to be updated.

Configuration: This test is run by default whenever both the reference and evaluation sets have associated labels.

Example: Suppose that the observed frequencies of the label column is [100, 200] in the reference set but [25, 150] in the test set. Then the PSI would be 0.201. If our PSI threshold was 0.1 then the test would fail.

Predicted Label Drift

This test checks that the difference in predicted label distribution between the reference and evaluation sets is small, using PSI test. The key detail displayed is the PSI statistic which is a measure of how different the frequencies of the column in the reference and evaluation sets are.

Why it matters: Predicted Label distribution shift between reference and test can indicate that the underlying data distribution has changed significantly enough to modify model decisions. This may mean that the model needs to be retrained to adjust to the new data environment. In addition, significant predicted label distribution shift may indicate that upstream decision-making modules (e.g. thresholds) may need to be updated.

Configuration: This test is run by default whenever the model or predictions is provided.

Example: Suppose that the observed frequencies of the predicted label column is [100, 200] in the reference set but [25, 150] in the test set. Then the PSI would be 0.201. If our PSI threshold was 0.1 then the test would fail.

Label Drift (Regression)

This test checks that the difference in label distribution between the reference and evaluation sets is small, using the PSI test. The key detail displayed is the KS statistic which is a measure of how different the labels in the reference and evaluation sets are. Concretely, the KS statistic is the maximum difference of the empirical CDF's of the two label columns.

Why it matters: Label distribution shift between reference and test can indicate that the underlying data distribution has changed significantly enough to modify model decisions. This may mean that the model needs to be retrained to adjust to the new data environment. In addition, significant label distribution shift may indicate that upstream decision-making modules (e.g. thresholds) may need to be updated.

Configuration: This test is run by default whenever both the reference and evaluation sets have associated labels.

Example: Suppose that the distribution of labels changes between the reference and evaluation sets such that PSI these two samples is 0.2. If the PSI threshold is 0.1, then this test would raise a warning.

Categorical Feature Drift

This test measures the severity of passing to the model data points that have categorical features which have drifted from the distribution observed in the reference set. The severity is a function of the impact on the model, as well as the presence of drift in the data. The model impact measures how much model performance changes due to drift in the given feature. The key detail displayed is the PSI test statistic, which is a measure of how statistically significant the difference between the frequencies of categorical values in the reference and evaluation sets is.

Why it matters: Distribution drift in categorical features between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the preprocessing pipeline. A big shift in categorical features towards categorical subsets that your model performs poorly in could indicate a degradation in model performance and signal the need for relabeling and retraining.

Configuration: By default, this test runs over all categorical columns with sufficiently many samples.

Example: Suppose that the observed frequencies of the isLoggedIn feature is [100, 200] in the reference set but [25, 150] in the test set. Then the PSI would be 0.201. If our PSI threshold was 0.1 then the test would fail.

Numeric Feature Drift

This test measures the severity of passing to the model data points that have numeric features that have drifted from the distribution observed in the reference set. The severity is a function of the impact on the model, as well as the presence of drift in the data. The model impact measures how much model performance changes due to drift in the given feature. The key detail is the Population Stability Index statistic. The Population Stability Index (PSI) is a measure of how different two distributions are. Given two distributions P and Q, it is computed as the sum of the KL Divergence between P and Q and the (reverse) KL Divergence between Q and P. Thus, PSI is symmetric.

Why it matters: Distribution shift between training and inference can cause degradation in model performance. If the shift is sufficiently large, retraining the model on newer data may be necessary.

Configuration: By default, this test runs over all numeric columns with sufficiently many samples and stored quantiles in each of the reference and evaluation sets.

Example: Suppose that the distribution of a feature Age changes between the reference and evaluation sets such that the Population Stability Index between these two samples is 0.2. If the distance threshold is set to 0.1, this test would raise a warning.

Prediction Drift

This test checks that the difference in the prediction distribution between the reference and evaluation sets is small, using Population Stability Index. The key detail displayed is the PSI which is a measure of how different the prediction distributions in the reference and evaluation sets are.

Why it matters: Prediction distribution shift between reference and test can indicate that the underlying data distribution has changed significantly enough to modify model decisions. This may mean that the model needs to be retrained to adjust to the new data environment. In addition, significant prediction distribution drift may indicate that upstream decision-making modules (e.g. thresholds) may need to be updated.

Configuration: This test is run by default whenever both the reference and evaluation sets have associated predictions. Different thresholds are associated with different severities.

Example: Suppose that the PSI between the prediction distributions in the reference and evaluation sets is 0.201. Then if the PSI thresholds are (0.1, 0.2, 0.3), the test would fail with medium severity.

Embedding Drift

This test measures the severity of passing to the model data points associated with embeddings that have drifted from the distribution observed in the reference set. The severity is a function of the impact on the model, as well as the presence of drift in the data. The model impact measures how much model performance changes due to drift in the given feature. The key detail is the Euclidean Distance statistic. The Euclidean Distance is defined as the square root of the sum of the squared differences between two vectors X and Y. The normalized version of this metric first divides each vector by its L2 norm. This test takes the normalized Euclidean distance between the centroids of the ref and eval data sets.

Why it matters: Distribution shift between training and inference can cause degradation in model performance. If the shift is sufficiently large, retraining the model on newer data may be necessary.

Configuration: By default, this test runs over all specified embeddings with sufficiently many samples in each of the reference and evaluation sets.

Example: Suppose that the distribution of an embedding User changes between the reference and evaluation sets such that the Euclidean Distance between these two samples is 0.3. If the distance threshold is set to 0.1, this test would raise a warning.

Nulls Per Feature Drift

This test measures the severity of passing to the model data points that have features with a null proportion that has drifted from the distribution observed in the reference set. The severity is a function of the impact on the model, as well as the presence of drift in the data. The model impact measures how much model performance changes due to drift in the given feature. The key detail is the p-value from a two-sample proportion test that checks if there is a statistically significant difference in the frequencies of null values between the reference and evaluation sets.

Why it matters: Distribution drift in null values between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the preprocessing pipeline. A big shift in null value proportion could indicate a degradation in model performance and signal the need for relabeling and retraining.

Configuration: By default, this test runs over all columns with sufficiently many samples.

Example: Suppose that the observed frequencies of the null values for a given feature is 100/2000 in the reference set but 100/1500 in the test. Then the p-value would be 0.0425. If our p-value threshold was 0.05 then the test would fail.

Nulls Per Row Drift

This test measures the severity of passing to the model data points that have proportions of null values that have drifted from the distribution observed in the reference set. The severity is a function of the impact on the model, as well as the presence of drift in the data. The model impact measures how much predictions change when the observed drift is applied to a given row. The key detail displayed is the PSI statistic that is a measure of how statistically significant the difference in the proportion of null values in a row between the reference and evaluation sets is.

Why it matters: Distribution drift in null values between training and inference can be caused by a variety of factors, including a change in the data generation process or a change in the preprocessing pipeline. A big shift in null value proportion could indicate a degradation in model performance and signal the need for relabeling and retraining.

Configuration: By default, this test runs over all rows.

Example: Suppose that in the reference set 5% of rows had more than three features that were null. If we observe in the evaluation set that now 50% of rows had more than three features that were null, this test would fail, highlighting a large drift in the proportion of features within a row that were null.

Attacks

Single-Feature Changes

This test measures the severity of passing to the model data points that have been manipulated across a single feature in an unbounded manner. The severity is a function of the impact of these manipulations on the model.

Why it matters: In production, your model will likely come across inputs that are out-of-distribution with respect to the training data, and it is often difficult to know ahead of time how your model will behave on such inputs. 'Attacking' a model in the manner of this test is a technique for finding the out-of-distribution regions of the input space where your model most severely misbehaves, before putting it into production. Rstricting ourselves to changing a single feature at a time is one proxy for what 'realistic' out-of-distribution data can look like.

Configuration: By default, for a given input we aim to change your model's prediction in the opposite direction of the true label. This test raises a warning if the average prediction change that can be achieved exceeds an acceptable threshold.

Example: Suppose your model has an Age feature with observed range 0 to 120. For every row in some sample, this test would search for the value of Age in 0 to 120 that caused the maximal change in prediction in the desired direction.

Bounded Single-Feature Changes

This test measures the severity of passing to the model data points that have been manipulated across a single feature in a bounded manner. The severity is a function of the impact of these manipulations on the model.We bound the manipulations to be less than some fraction of the range of the given feature.

Why it matters: In production, your model will likely come across inputs that are out-of-distribution with respect to the training data, and it is often difficult to know ahead of time how your model will behave on such inputs. 'Attacking' a model in the manner of this test is a technique for finding the out-of-distribution regions of the input space where your model most severely misbehaves, before putting it into production. Restricting ourselves to changing a single feature by a small amount is one proxy for what 'realistic' out-of-distribution data can look like.

Configuration: By default, for a given input we aim to change your model's prediction in the opposite direction of the true label. This test raises a warning if the average prediction change that can be achieved exceeds an acceptable threshold. This test runs only over numeric features.

Example: Suppose your model has an Age feature with observed range 0 to 120, and we restricted ourselves to changes that were no greater than 10% of the feature range. For every row in some sample, this test would search for the value of Age that was at most 12 away from the row's initial Age value and that caused the maximal change in prediction in the desired direction.

Multi-Feature Changes

This test measures the severity of passing to the model data points that have been manipulated across multiple features in an unbounded manner. The severity is a function of the impact of these manipulations on the model.

Why it matters: In production, your model will likely come across inputs that are out-of-distribution with respect to the training data, and it is often difficult to know ahead of time how your model will behave on such inputs. 'Attacking' a model in the manner of this test is a technique for finding the out-of-distribution regions of the input space where your model most severely misbehaves, before putting it into production. Restricting the number of features that can be changed is one proxy for what 'realistic' out-of-distribution data can look like.

Configuration: By default, for a given input we aim to change your model's prediction in the opposite direction of the true label. This test raises a warning if the average prediction change that can be achieved exceeds an acceptable threshold.

Example: Suppose we restricted ourselves to changing 5 features. This means for each input we would search for the 5 feature values change that, when performed together, caused the largest possible change in your model's prediction on that input.

Bounded Multi-Feature Changes

This test measures the severity of passing to the model data points that have been manipulated across multiple features in an bounded manner. The severity is a function of the impact of these manipulations on the model.We bound the manipulations to be less than some fraction of the range of the given feature.

Why it matters: In production, your model will likely come across inputs that are out-of-distribution with respect to the training data, and it is often difficult to know ahead of time how your model will behave on such inputs. 'Attacking' a model in the manner of this test is a technique for finding the out-of-distribution regions of the input space where your model most severely misbehaves, before putting it into production. Restricting the number of features that can be changed and the magnitude of the change that can be made to each feature is one proxy for what 'realistic' out-of-distribution data can look like.

Configuration: By default, for a given input we aim to change your model's prediction in the opposite direction of the true label. This test raises a warning if the average prediction change that can be achieved exceeds an acceptable threshold. This test runs only over numeric features.

Example: Suppose we restricted ourselves to changing 5 features, each by no more than 10% of the range of the given feature. This means for each input we would search for the 5 restricted feature values change that, when performed together, caused the largest possible change in your model's prediction on that input.

HopSkipJump Attack

This test measures the robustness of your model to HopSkipJump attacks. It does this by taking a sample of inputs, applying a HopSkipJump attack to each input, and measuring the performance of the model on the perturbed input. See the paper "HopSkipJumpAttack: A Query-Efficient Decision-Based Attack" by Chen, et al. (https://arxiv.org/abs/1904.02144) for more details.

Why it matters: Malicious actors can perturb input data to alter model behavior in unexpected ways. It is important that your models are robust to such attacks.

Configuration: By default, this test runs when the "Attacks" test category is selected.

Data Cleanliness

Label Imbalance

This test checks that no labels have exceedingly high frequency.

Why it matters: Label imbalance in the training data can introduce bias into the model and possibly result in poor predictive performance on examples from the minority classes.

Configuration: This test runs only on classification tasks.

Example: Suppose we had a binary classification task. We can configure this test to check that neither label 0 nor 1 has frequency above a certain threshold.

Required Features

This test checks that the features of a dataset are as expected.

Why it matters: Errors in data collection and processing can lead to invalid missing (or extra) features. In the case of missing features, this can cause failures in models. In the case of extra features, this can lead to unnecessary storage and computation.

Configuration: This test runs only when required features are specified.

Example: Suppose we had a few features (Age, Location, etc.) that we always expected to be present in the dataset. We can configure this test to check that those columns are there.

Duplicate Row

This test checks if there are any duplicate rows in your dataset. The key detail displays the number of duplicate rows in your dataset.

Why it matters: Duplicate rows are potentially a sign of a broken data pipeline or an otherwise corrupted input.

Configuration: By default this test is run over all features, meaning two rows are considered duplicates only if they match across all features.

Example: Suppose we had two rows that were the same across every feature except an ID feature. By default these two rows would not be flagged as duplicates. If we exclude the ID feature, then these two rows would be flagged as duplicates.

Mutual Information Decrease (Feature to Label)

This test flags a likely data leakage issue in the model.Data leakage occurs when a model is trained on features containing information about the label that is not normally present during production.This test flags an issue if both of the following occur:

• the normalized mutual information between the feature and the label is too high in the reference set
• the normalized mutual information for the reference set is much higher than for the evaluation set

The first criterion is an indicator that the feature has unreasonably high predictive power for the label during training, and the second criterion checks that the feature is no longer a good predictor in the evaluation set. One requirement for this test to flag data leakage is that the evaluation set labels and features are collected properly. This test should be utilized if one trusts their eval data is collected correctly, else the High MI test should be used.

Why it matters: Errors in data collection and processing can lead to some features containing information about the label in the reference set that do not appear in the evaluation set. This causes the model to under-perform during production.

Configuration: By default, this test always runs on all categorical features.

Example: Consider a lending model that is trying to predict a boolean variable loan given that reports whether or not a bank will issue this loan to a potential borrower, and suppose one of the features is total debt over 50K. An error during the data processing causes the model to be trained on a data set where total debt over 50K is calculated after the loan has already been given, resulting in the model predicting loan given to be true whenever total debt over 50K is large. However, when the model is deployed, the feature total debt must be calculated before the loan given prediction can be made.
The normalized mutual information between these columns might be 0.3 in the reference set but only 0.1 in the evaluation set. This test would then flag a likely feature leakage issue where total debt over 50K is leaking into the variable loan given during training.

High Mutual Information (Feature to Label)

This test flags a likely data leakage issue if the normalized mutual information between the feature and the label is too high in the reference set. Data leakage occurs when a model is trained on features containing information about the label that is not normally present during production. This criterion is an indicator that this feature has unreasonably high predictive power for the label during training. One requirement for this test to flag data leakage is that the reference set labels and features are collected properly. This test should be utilized when one doesn't trust their eval data is collected correctly, else the MI Decrease test should be used.

Why it matters: Errors in data collection and processing can lead to some features containing information about the label in the reference set. This causes the model to under-perform during production.

Configuration: By default, this test always runs on all categorical features.

Example: Consider a lending model that is trying to predict a boolean variable loan given that reports whether or not a bank will issue this loan to a potential borrower, and suppose one of the features is total debt over 50K. An error during the data processing causes the model to be trained on a data set where total debt over 50K is calculated after the loan has already been given, resulting in the model predicting loan given to be true whenever total debt over 50K is true. The normalized mutual information between these columns might be 0.8 in the reference set, due to the data leakage phenomenon. This test would then flag a likely feature leakage issue where total debt over 50K is leaking into the variable loan given during training.

High Feature Correlation

This test checks that the correlation between two features in the reference set is not too high. Correlation is a measure of the linear relationship between two numeric features.

Why it matters: Correlation in training features can be caused by a variety of factors, including interdependencies between the collected features, data collection processes, or change in data labeling. Training on too similar features can lead to underperforming or non-robust models.

Configuration: By default, this test runs over all pairs of numeric features in the dataset.

Example: Suppose that the correlation between age and years of employment is 0.9 in the reference set. Because of the high correlation between this pair of features, you might not want to train a model across both of them, and this test would fail.

Subset Performance

Subset AUC

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Area Under Curve (AUC) of model predictions within a specific subset is significantly lower than the model prediction Area Under Curve (AUC) over the entire population.

Why it matters: Having different AUC between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, AUC is computed over all predictions/labels. Note that we compute AUC of the Receiver Operating Characteristic (ROC) curve.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the AUC over the feature subset value 'cat' would be 0.0, compared to the overall metric of 0.44.

Subset Prediction Variance (Negative Labels)

The subset variance test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the variance of model predictions within a specific subset is significantly higher than model prediction variance of the entire population. In this test, the population refers to all data with negative ground-truth labels.

Why it matters: High variance within a feature subset compared to the overall population could mean a few different things, and should be analyzed with other subset performance tests (accuracy, AUC) for a more clear view. In the variance metric over positive/negative labels, this could mean the model is much more uncertain about the given subset. When paired with a decrease in AUC, this implies the model underperforms on this subset.

Configuration: By default, the variance is computed over all predictions with a negative ground-truth label.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]] and model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.48]. Assume the labels are [1, 0, 1, 0, 0, 0].Then the prediction variance for feature column 1, subset 'cat' with negative labels would be 0.

Subset Multiclass AUC

In the multiclass setting, we compute one vs. one area under the curve (AUC), which computes the AUC between every pairwise combination of classes. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Area Under Curve (AUC) of model predictions within a specific subset is significantly lower than the model prediction Area Under Curve (AUC) over the entire population.

Why it matters: Having different AUC between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, AUC is computed over all predictions/labels. Note that we compute AUC of the Receiver Operating Characteristic (ROC) curve.

Example: Suppose we are differentiating between cats, bears, and dogs. Assume that across the data points where height=2 the predictions are [0.9, 0.1, 0], [0.1, 0.9, 0], [0.2, 0.1, 0.7] and the labels are [1, 0, 0], [1, 0, 0], [0, 0, 1] (where the first index corresponds to cat, the second corresponds to bear, and the third corresponds to dog). Then the AUC (one vs. one) across this subset is 0.75. If the overall AUC (one vs. one) across all subsets is 0.9 then this test raises a warning.

Subset Mean Reciprocal Rank (MRR)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the MRR of model predictions within a specific subset is significantly lower than the model prediction MRR over the entire population.

Why it matters: Having different MRR between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, MRR is computed over all predictions/labels.

Example: Suppose we had the following query-document pairs: [[(qid: 1), 'A'], [(qid: 1), 'A'], [(qid: 2), 'B'], [(qid: 2), 'B']], model predictions [2, 1, 1, 2], and true relevance ranks [1,2,1,2]. Then, the MRR over the feature subset 'A' would be 0.5, compared to the overall metric of 0.75.

Subset Mean-Absolute Percentage Error (MAPE)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the MAPE of model predictions within a specific subset is significantly higher than the model prediction MAPE over the entire population.

Why it matters: Having different mean-absolute percentage error between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, mean-absolute percentage error is computed over all predictions/labels.

Example: Suppose we had data with 2 features: [[0.4, 0.2], [0.5, 0.3], [0.7, 0.5], [0.6, 0.7], [0.8, 0.7]], model predictions [0.3, 0.4, 0.8, 0.8, 0.9], and labels [0.5, 0.5, 1.5, 1.5, 1.5]. Then, the Mean-absolute percentage error over the feature subset (0.0, 0.5] for the first feature would be 0.15, compared to the overall metric of 0.46.

Subset Root-Mean-Squared Error (RMSE)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the RMSE of model predictions within a specific subset is significantly higher than the model prediction RMSE over the entire population.

Why it matters: Having different RMSE between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, RMSE is computed over all predictions/labels.

Example: Suppose we had data with 2 features: [[0.4, 0.2], [0.5, 0.3], [0.7, 0.5], [0.6, 0.7], [0.8, 0.7]], model predictions [0.3, 0.4, 0.8, 0.8, 0.9], and labels [0.5, 0.5, 1.5, 1.5, 1.5]. Then, the RMSE over the feature subset (0.0, 0.5] for the first feature would be 0.158, compared to the overall metric of 0.527.

Subset Macro Precision

The precision test is also popularly referred to as positive predictive parity in fairness literature. When transitioning to the multiclass setting, we can compute macro precision which computes the precisions of each class individually and then averages them.This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Macro Precision of model predictions within a specific subset is significantly lower than the model prediction Macro Precision over the entire population.

Why it matters: Having different macro precision (e.g. false discovery rates) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. Note that positive predictive parity does not necessarily indicate equal opportunity or predictive equality: as a hypothetical example, imagine that a loan qualification classifier flags 100 entries for group A and 100 entries for group B, each with a precision of 100%, but there are 100 actual qualified entries in group A and 9000 in group B. This would indicate disparities in opportunities given to each subgroup.

Configuration: By default, Macro Precision is computed over all predictions/labels. Note that the predicted label is the label with the greatest predicted probability.

Example: Suppose we are differentiating between cats, bears, and dogs. Assume that across the data points where height=2 the predictions are [0.9, 0.1, 0], [0.1, 0.9, 0], [0.2, 0.1, 0.7] and the labels are [1, 0, 0], [1, 0, 0], [0, 0, 1] (where the first index corresponds to cat, the second corresponds to bear, and the third corresponds to dog). Then the Macro Precision across this subset is 0.67. If the overall Macro Precision across all subsets is 0.9 then this test raises a warning.

Subset Accuracy

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the accuracy of model predictions within a specific subset is significantly lower than the model prediction accuracy over the entire population.

Why it matters: Having different accuracy between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Accuracy can be thought of as a 'weaker' metric of model bias compared to measuring false positive rate (predictive equality) or false negative rate (equal opportunity). This is because we can have similar accuracy between group A and group B; yet group A actually has higher false positive rate, while group B has higher false negative rate (e.g. we reject qualified applicants in group A but accept non-qualified applicants in group B). Nevertheless, accuracy is a standard metric used during evaluation and should be considered as part of performance bias testing.

Configuration: By default, accuracy is computed over all predictions/labels. Note we round predictions to 0/1 to compute accuracy.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the accuracy over the feature subset value 'cat' would be 0.33, compared to the overall metric of 0.5.

Subset Average Confidence

Default long description for subset batch runner.

Subset Recall

The recall test is more popularly referred to as equal opportunity or false negative error rate balance in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Recall of model predictions within a specific subset is significantly lower than the model prediction Recall over the entire population.

Why it matters: Having different true positive rates (e.g. equal opportunity) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. An intuitive example is when the label indicates a positive attribute: if predicting whether to interview a given candidate, make sure that out of qualified candidates, the rate at which the model predicts a rejection is similar to group A and B.

Configuration: By default, Recall is computed over all predictions/labels. Note that we round predictions to 0/1 to compute recall.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the Recall over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.66.

Subset Normalized Discounted Cumulative Gain (NDCG)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the NDCG of model predictions within a specific subset is significantly lower than the model prediction NDCG over the entire population.

Why it matters: Having different NDCG between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, NDCG is computed over all predictions/labels.

Example: Suppose we had the following query-document pairs: [[(qid: 1), 'A'], [(qid: 1), 'A'], [(qid: 2), 'B'], [(qid: 2), 'B']], model predictions [2, 1, 1, 2], and true relevance ranks [1,2,1,2]. Then, the NDCG over the feature subset 'A' would be 0.86, compared to the overall metric of 0.93.

Subset Prediction Variance

Default long description for subset batch runner.

Subset Precision

The precision test is also popularly referred to as positive predictive parity in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Precision of model predictions within a specific subset is significantly lower than the model prediction Precision over the entire population.

Why it matters: Having different precision (e.g. false discovery rates) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. Note that positive predictive parity does not necessarily indicate equal opportunity or predictive equality: as a hypothetical example, imagine that a loan qualification classifier flags 100 entries for group A and 100 entries for group B, each with a precision of 100%, but there are 100 actual qualified entries in group A and 9000 in group B. This would indicate disparities in opportunities given to each subgroup.

Configuration: By default, Precision is computed over all predictions/labels. Note that we round predictions to 0/1 to compute precision.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the Precision over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.5.

Subset False Negative Rate

Default long description for subset batch runner.

Subset Mean-Squared-Log Error (MSLE)

Default long description for subset batch runner.

Subset Multiclass Accuracy

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the accuracy of model predictions within a specific subset is significantly lower than the model prediction accuracy over the entire population.

Why it matters: Having different accuracy between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Accuracy can be thought of as a 'weaker' metric of model bias compared to measuring false positive rate (predictive equality) or false negative rate (equal opportunity). This is because we can have similar accuracy between group A and group B; yet group A actually has higher false positive rate, while group B has higher false negative rate (e.g. we reject qualified applicants in group A but accept non-qualified applicants in group B). Nevertheless, accuracy is a standard metric used during evaluation and should be considered as part of performance bias testing.

Configuration: By default, accuracy is computed over all predictions/labels. Note we round predictions to 0/1 to compute accuracy.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the accuracy over the feature subset value 'cat' would be 0.33, compared to the overall metric of 0.5.

Subset Rank Correlation

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the rank correlation of model predictions within a specific subset is significantly lower than the model prediction rank correlation over the entire population.

Why it matters: Having different rank correlation between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, rank correlation is computed over all predictions/labels.

Example: Suppose we had the following query-document pairs: [[(qid: 1), 'A'], [(qid: 1), 'A'], [(qid: 2), 'B'], [(qid: 2), 'B']], model predictions [2, 1, 1, 2], and true relevance ranks [1,2,1,2]. Then, the rank correlation over the feature subset 'A' would be -1, compared to the overall metric of 0.

Subset Mean-Absolute Error (MAE)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the MAE of model predictions within a specific subset is significantly higher than the model prediction MAE over the entire population.

Why it matters: Having different mean-absolute error between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, mean-absolute error is computed over all predictions/labels.

Example: Suppose we had data with 2 features: [[0.4, 0.2], [0.5, 0.3], [0.7, 0.5], [0.6, 0.7], [0.8, 0.7]], model predictions [0.3, 0.4, 0.8, 0.8, 0.9], and labels [0.5, 0.5, 1.5, 1.5, 1.5]. Then, the Mean-absolute error over the feature subset (0.0, 0.5] for the first feature would be 0.15, compared to the overall metric of 0.46.

Subset Macro Recall

The recall test is more popularly referred to as equal opportunity or false negative error rate balance in fairness literature. When transitioning to the multiclass setting we can use macro recall which computes the recall of each individual class and then averages these numbers.This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Macro Recall of model predictions within a specific subset is significantly lower than the model prediction Macro Recall over the entire population.

Why it matters: Having different true positive rates (e.g. equal opportunity) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. An intuitive example is when the label indicates a positive attribute: if predicting whether to interview a given candidate, make sure that out of qualified candidates, the rate at which the model predicts an interview is similar to group A and B.

Configuration: By default, Macro Recall is computed over all predictions/labels. Note that the predicted label is the label with the largest predicted class probability.

Example: Suppose we are differentiating between cats, bears, and dogs. Assume that across the data points where height=2 the predictions are [0.9, 0.1, 0], [0.1, 0.9, 0], [0.2, 0.1, 0.7] and the labels are [1, 0, 0], [1, 0, 0], [0, 0, 1] (where the first index corresponds to cat, the second corresponds to bear, and the third corresponds to dog). Then the Macro Recall across this subset is 0.67. If the overall Macro Recall across all subsets is 0.9 then this test raises a warning.

Subset Mean-Squared Error (MSE)

Default long description for subset batch runner.

Subset Macro F1

F1 is a holistic measure of both precision and recall. When transitioning to the multiclass setting we can use macro F1 which computes the F1 of each class and averages them. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the macro F1 of model predictions within a specific subset is significantly lower than the model prediction macro F1 over the entire population.

Why it matters: Having different macro F1 between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, macro F1 is computed over all predictions/labels. Note that the predicted label is the label with the largest predicted probability.

Example: Suppose we are differentiating between cats, bears, and dogs. Assume that across the data points where height=2 the predictions are [0.9, 0.1, 0], [0.1, 0.9, 0], [0.2, 0.1, 0.7] and the labels are [1, 0, 0], [1, 0, 0], [0, 0, 1] (where the first index corresponds to cat, the second corresponds to bear, and the third corresponds to dog). Then the macro F1 across this subset is 0.78. If the overall macro F1 across all subsets is 0.9 then this test raises a warning.

Subset F1

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the F1 of model predictions within a specific subset is significantly lower than the model prediction F1 over the entire population.

Why it matters: Having different F1 between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, F1 is computed over all predictions/labels. Note that we round predictions to 0/1 to compute F1 score.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the F1 over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.57.

Subset Prediction Variance (Positive Labels)

The subset variance test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the variance of model predictions within a specific subset is significantly higher than model prediction variance of the entire population. In this test, the population refers to all data with positive ground-truth labels.

Why it matters: High variance within a feature subset compared to the overall population could mean a few different things, and should be analyzed with other subset performance tests (accuracy, AUC) for a more clear view. In the variance metric over positive/negative labels, this could mean the model is much more uncertain about the given subset. When paired with a decrease in AUC, this implies the model underperforms on this subset.

Configuration: By default, the variance is computed over all predictions with a positive ground-truth label.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]] and model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.48]. Assume the labels are [1, 0, 1, 0, 0, 0].Then the prediction variance for feature column 1, subset 'cat' with positive labels would be 0.04.

Subset False Positive Rate

The false positive error rate test is also popularly referred to as as predictive equality, or equal mis-opportunity in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the false positive rate of model predictions within a specific subset is significantly higher than the model prediction false positive rate over the entire population.

Why it matters: Having different false positive rates (e.g. predictive equality) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. As an intuitive example, consider the case when the label indicates an undesirable attribute: if predicting whether a person will default on their loan, make sure that for people who didn't default, the rate at which the model incorrectly predicts positive is similar for group A and B.

Configuration: By default, false positive rate is computed over all predictions/labels. Note that we round predictions to 0/1 to compute false positive rate.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the false positive rate over the feature subset value 'cat' would be 1.0, compared to the overall metric of 0.67.

Subset Average Rank

Default long description for subset batch runner.

Subset Positive Prediction Rate

Default long description for subset batch runner.

Abnormal Inputs

Numeric Outliers

This test measures the number of failing rows in your data with outliers and their impact on the model. Outliers are values which may not necessarily be outside of an allowed range for a feature, but are extreme values that are unusual and may be indicative of abnormality. The model impact is the difference in model performance between passing and failing rows with outliers. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: Outliers can be a sign of corrupted or otherwise erroneous data, and can degrade model performance if used in the training data, or lead to unexpected behaviour if input at inference time.

Configuration: By default this test is run over each numeric feature that is neither unique nor ascending.

Example: Suppose there is a feature age for which in the reference set the values 103 and 114 each appear once but every other value (with substantial sample size) is contained within the range [0, 97]. Then we would infer a lower outlier threshold of 0 and an upper outlier threshold of 97. This test raises a warning if we observe any values in the evaluation set outside these thresholds or if model performance decreases on observed datapoints with outliers.

Unseen Categorical

This test measures the number of failing rows in your data with unseen categorical values and their impact on the model. The model impact is the difference in model performance between passing and failing rows with unseen categorical values. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all categorical features.

Example: Say that the feature Animal contains the values ['Cat', 'Dog'] from the reference set. This test raises a warning if we observe any unseen values in the evaluation set such as 'Mouse' that causes a significant change in model performance. If labels/predictions are provided in the run, then a severity would be raised if the Average Prediction changed by 0.03. If labels/predictions were not provided but 'Mouse' appeared in 3% of the evaluation dataset, an severity would be raised due to the significant increase in presence of an unseen feature.

Unseen Domain

This test measures the number of failing rows in your data with unseen domain values and their impact on the model. The model impact is the difference in model performance between passing and failing rows with unseen domain values. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all features inferred to contain domains.

Example: Say that the feature WebDomain contains the values ['gmail.com', 'hotmail.com'] from the reference set. This test raises a warning if we observe any unseen values in the evaluation set such as 'xyzabc.com' that causes a significant change in model performance. If labels/predictions are provided in the run, then a severity issue would be raised if the Average Prediction changed by 0.03. If labels/predictions were not provided but 'xyzabc.com' appeared in 3% of the evaluation dataset, an severity issue would be raised due to the significant increase in presence of an unseen feature.

Unseen Email

This test measures the number of failing rows in your data with unseen email values and their impact on the model. The model impact is the difference in model performance between passing and failing rows with unseen email values. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all features inferred to contain emails.

Example: Say that the feature Email contains the values ['[email protected]', '[email protected]'] from the reference set. This test raises a warning if we observe any unseen values in the evaluation set such as '[email protected]' that causes a significant change in model performance. If labels/predictions are provided in the run, then a severity issue would be raised if the Average Prediction changed by 0.03. If labels/predictions were not provided but '[email protected]' appeared in 3% of the evaluation dataset, a severity issue would be raised due to the significant increase in presence of an unseen feature.

Unseen URL

This test measures the number of failing rows in your data with unseen URL values and their impact on the model. The model impact is the difference in model performance between passing and failing rows with unseen URL values. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: Unseen categorical values are a common failure point in machine learning systems; since these models are trained over a reference set, they may yield uninterpretable or undefined behavior when interacting with an unseen categorical value. In addition, such errors may expose gaps or errors in data collection.

Configuration: By default, this test runs over all features inferred to contain URLs.

Example: Say that the feature WebURL contains the values ['http://google.com', 'http://yahoo.com'] from the reference set. This test raises a warning if we observe any unseen values in the evaluation set such as 'http://xyzabc.com' that causes a significant change in model performance. If labels/predictions are provided in the run, then a severity issue would be raised if the Average Prediction changed by 0.03. If labels/predictions were not provided but 'xyzabc.com' appeared in 3% of the evaluation dataset, an severity issue would be raised due to the significant increase in presence of an unseen feature.

Rare Categories

This test measures the severity of passing to the model data points whose features contain rarely observed categories (relative to the reference set). The severity is a function of the impact of these values on the model, as well as the presence of these values in the data. The model impact is the difference in model performance between passing and failing rows with rarely observed categorical values. If labels are not provided, prediction change is used instead of model performance change. The number of failing rows refers to the number of times rarely observed categorical values are observed in the evaluation set.

Why it matters: Rare categories are a common failure point in machine learning systems because less data often means worse performance. In addition, this may expose gaps or errors in data collection.

Configuration: By default, this test runs over all categorical features. A category is considered rare if it occurs fewer than min_num_occurrences times, or if it occurs less than min_pct_occurrences of the time. If neither of these values are specified, the rate of appearance below which a category is considered rare is min_ratio_rel_uniform divided by the number of classes.

Example: Say that the feature AgeGroup takes on the value 0-18 twice while taking on the value 35-55 a total of 98 times. If the min_num_occurences is 5 and the min_pct_occurrences is 0.03 then the test will flag the value 0-18 as a rare category.

Out of Range

This test measures the number of failing rows in your data with values outside the inferred range of allowed values and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values outside the inferred range of allowed values. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: In production, the model may encounter corrupted or manipulated out of range values. It is important that the model is robust to such extremities.

Configuration: By default, this test runs over all numeric features.

Example: In the reference set, the Age feature has a range of [0, 121]. This test raises a warning if we observe values outside of this range in the evaluation set (eg. 150, 200) or if model performance decreases on observed datapoints outside of this range.

Required Characters

This test measures the number of failing rows in your data with strings without any required characters and their impact on the model. The model impact is the difference in model performance between passing and failing rows with strings without any required characters. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require specific characters. However, errors in the data pipeline may allow invalid data points that lack these required characters to pass. Failing to catch such errors may lead to noisier training data or noisier predictions during inference, which can degrade model metrics.

Configuration: By default, this test runs over all string features that are inferred to have required characters.

Example: Say that the feature email requires the character @. This test raises a warning if we observe any values in the evaluation set where the character is missing.

Inconsistencies

This test measures the severity of passing to the model data points whose values are inconsistent (as inferred from the reference set). The severity is a function of the impact of these values on the model, as well as the presence of these values in the data. The model impact is the difference in model performance between passing and failing rows with data containing inconsistent feature values. If labels are not provided, prediction change is used instead of model performance change. The number of failing rows refers to the number of times data containing inconsistent feature values are observed in the evaluation set.

Why it matters: Inconsistent values might be the result of malicious actors manipulating the data or errors in the data pipeline. Thus, it is important to be aware of inconsistent values to identify sources of manipulations or errors.

Configuration: By default, this test runs on pairs of categorical features whose correlations exceed some minimum threshold. The default threshold for the frequency ratio below which values are considered to be inconsistent is 0.02.

Example: Suppose we have a feature country that takes on value "US" with frequency 0.5, and a feature time_zone that takes on value "Central European Time" with frequency 0.2. Then if these values appear together with frequency less than 0.5 * 0.2 * 0.02 = 0.002 , in the reference set, rows in which these values do appear together are inconsistencies.

Capitalization

This test measures the number of failing rows in your data with different types of capitalization and their impact on the model. The model impact is the difference in model performance between passing and failing rows with different types of capitalization. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: In production, models can come across the same value with different capitalizations, making it important to explicitly check that your model is invariant to such differences.

Configuration: By default, this test runs over all categorical features.

Example: Suppose we had a column that corresponded to country code. For a specific row, let's say the observed value in the reference set was USA. This test raises a warning if we observe a similar value in the evaluation set with case changes, e.g. uSa or if model performance decreases on observed datapoints with case changes.

Empty String

This test measures the number of failing rows in your data with empty string values instead of null values and their impact on the model. The model impact is the difference in model performance between passing and failing rows with empty string values instead of null values. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: In production, the model may encounter corrupted or manipulated string values. Null values and empty strings are often expected to be treated the same, but the model might not treat them that way. It is important that the model is robust to such extremities.

Configuration: By default, this test runs over all string features with null values.

Example: In the reference set, the Name feature contains nulls. This test raises a warning if we observe any empty string in the Name feature or if these values decrease model performance.

Embedding Anomalies

This test measures the number of failing rows in your data with anomalous embeddings and their impact on the model. The model impact is the difference in model performance between passing and failing rows with anomalous embeddings. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: In production, the presence of anomalous embeddings can indicate breaks in upstream data pipelines, poor model generalization, or other issues.

Configuration: By default, this test runs over all configured embeddings.

Example: Say that the 'user_id' embedding is two-dimensional and has a mean at the origin and a covariance matrix of [[1, 0], [0, 1]] in the reference set. This test will flag any embeddings in the test set that are distant from the reference distribution using the Mahalanobis distance.

Null Check

This test measures the number of failing rows in your data with nulls in features that should not have nulls and their impact on the model. The model impact is the difference in model performance between passing and failing rows with nulls in features that should not have nulls. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: The model may make certain assumptions about a column depending on whether or not it had nulls in the training data. If these assumptions break during production, this may damage the model's performance. For example, if a column was never null during training then a model may not have learned to be robust against noise in that column.

Configuration: By default, this test runs over all columns that had zero nulls in the reference set.

Example: Suppose that the feature Age was never null in the reference set. This test raises a warning if Age was null 10% of the time in the evaluation set or if model performance decreases on observed datapoints with nulls

Must be Int

This test measures the number of failing rows in your data with values not of type Integer and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type Integer. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Integer.

Example: Say that the feature X requires the Integer type. This test raises a warning if we observe any values where X is represented as a different type instead.

Must be Float

This test measures the number of failing rows in your data with values not of type Float and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type Float. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Float.

Example: Say that the feature X requires the Float type. This test raises a warning if we observe any values where X is represented as a different type instead.

Must be String

This test measures the number of failing rows in your data with values not of type String Categorical and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type String Categorical. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type String Categorical.

Example: Say that the feature X requires the String Categorical type. This test raises a warning if we observe any values where X is represented as a different type instead.

Must be Boolean

This test measures the number of failing rows in your data with values not of type Boolean Categorical and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type Boolean Categorical. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Boolean Categorical.

Example: Say that the feature X requires the Boolean Categorical type. This test raises a warning if we observe any values where X is represented as a different type instead.

Must be URL

This test measures the number of failing rows in your data with values not of type URL Categorical and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type URL Categorical. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type URL Categorical.

Example: Say that the feature X requires the URL Categorical type. This test raises a warning if we observe any values where X is represented as a different type instead.

Must be Domain

This test measures the number of failing rows in your data with values not of type Domain Categorical and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type Domain Categorical. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Domain Categorical.

Example: Say that the feature X requires the Domain Categorical type. This test raises a warning if we observe any values where X is represented as a different type instead.

Must be Email

This test measures the number of failing rows in your data with values not of type Email Categorical and their impact on the model. The model impact is the difference in model performance between passing and failing rows with values not of type Email Categorical. If labels are not provided, prediction change is used instead of model performance change.

Why it matters: A feature may require a specific type. However, errors in the data pipeline may produce values that are outside the expected type. Failing to catch such errors may lead to errors or undefined behavior from the model.

Configuration: By default, this test runs over all features that are inferred to be type Email Categorical.

Example: Say that the feature X requires the Email Categorical type. This test raises a warning if we observe any values where X is represented as a different type instead.

Subset Performance Degradation

Subset Drift AUC

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Area Under Curve (AUC) of model predictions within a specific subset is significantly lower than the model prediction Area Under Curve (AUC) over the entire population.

Why it matters: Having different AUC between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, AUC is computed over all predictions/labels. Note that we compute AUC of the Receiver Operating Characteristic (ROC) curve.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the AUC over the feature subset value 'cat' would be 0.0, compared to the overall metric of 0.44.

Subset Drift Prediction Variance (Negative Labels)

The subset variance test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the variance of model predictions within a specific subset is significantly higher than model prediction variance of the entire population. In this test, the population refers to all data with negative ground-truth labels.

Why it matters: High variance within a feature subset compared to the overall population could mean a few different things, and should be analyzed with other subset performance tests (accuracy, AUC) for a more clear view. In the variance metric over positive/negative labels, this could mean the model is much more uncertain about the given subset. When paired with a decrease in AUC, this implies the model underperforms on this subset.

Configuration: By default, the variance is computed over all predictions with a negative ground-truth label.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]] and model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.48]. Assume the labels are [1, 0, 1, 0, 0, 0].Then the prediction variance for feature column 1, subset 'cat' with negative labels would be 0.

Subset Drift Mean Reciprocal Rank (MRR)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the MRR of model predictions within a specific subset is significantly lower than the model prediction MRR over the entire population.

Why it matters: Having different MRR between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, MRR is computed over all predictions/labels.

Example: Suppose we had the following query-document pairs: [[(qid: 1), 'A'], [(qid: 1), 'A'], [(qid: 2), 'B'], [(qid: 2), 'B']], model predictions [2, 1, 1, 2], and true relevance ranks [1,2,1,2]. Then, the MRR over the feature subset 'A' would be 0.5, compared to the overall metric of 0.75.

Subset Drift Mean-Absolute Percentage Error (MAPE)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the MAPE of model predictions within a specific subset is significantly higher than the model prediction MAPE over the entire population.

Why it matters: Having different mean-absolute percentage error between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, mean-absolute percentage error is computed over all predictions/labels.

Example: Suppose we had data with 2 features: [[0.4, 0.2], [0.5, 0.3], [0.7, 0.5], [0.6, 0.7], [0.8, 0.7]], model predictions [0.3, 0.4, 0.8, 0.8, 0.9], and labels [0.5, 0.5, 1.5, 1.5, 1.5]. Then, the Mean-absolute percentage error over the feature subset (0.0, 0.5] for the first feature would be 0.15, compared to the overall metric of 0.46.

Subset Drift Root-Mean-Squared Error (RMSE)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the RMSE of model predictions within a specific subset is significantly higher than the model prediction RMSE over the entire population.

Why it matters: Having different RMSE between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, RMSE is computed over all predictions/labels.

Example: Suppose we had data with 2 features: [[0.4, 0.2], [0.5, 0.3], [0.7, 0.5], [0.6, 0.7], [0.8, 0.7]], model predictions [0.3, 0.4, 0.8, 0.8, 0.9], and labels [0.5, 0.5, 1.5, 1.5, 1.5]. Then, the RMSE over the feature subset (0.0, 0.5] for the first feature would be 0.158, compared to the overall metric of 0.527.

Subset Drift Accuracy

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the accuracy of model predictions within a specific subset is significantly lower than the model prediction accuracy over the entire population.

Why it matters: Having different accuracy between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Accuracy can be thought of as a 'weaker' metric of model bias compared to measuring false positive rate (predictive equality) or false negative rate (equal opportunity). This is because we can have similar accuracy between group A and group B; yet group A actually has higher false positive rate, while group B has higher false negative rate (e.g. we reject qualified applicants in group A but accept non-qualified applicants in group B). Nevertheless, accuracy is a standard metric used during evaluation and should be considered as part of performance bias testing.

Configuration: By default, accuracy is computed over all predictions/labels. Note we round predictions to 0/1 to compute accuracy.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the accuracy over the feature subset value 'cat' would be 0.33, compared to the overall metric of 0.5.

Subset Drift Recall

The recall test is more popularly referred to as equal opportunity or false negative error rate balance in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Recall of model predictions within a specific subset is significantly lower than the model prediction Recall over the entire population.

Why it matters: Having different true positive rates (e.g. equal opportunity) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. An intuitive example is when the label indicates a positive attribute: if predicting whether to interview a given candidate, make sure that out of qualified candidates, the rate at which the model predicts a rejection is similar to group A and B.

Configuration: By default, Recall is computed over all predictions/labels. Note that we round predictions to 0/1 to compute recall.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the Recall over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.66.

Subset Drift NDCG

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the NDCG of model predictions within a specific subset is significantly lower than the model prediction NDCG over the entire population.

Why it matters: Having different NDCG between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, NDCG is computed over all predictions/labels.

Example: Suppose we had the following query-document pairs: [[(qid: 1), 'A'], [(qid: 1), 'A'], [(qid: 2), 'B'], [(qid: 2), 'B']], model predictions [2, 1, 1, 2], and true relevance ranks [1,2,1,2]. Then, the NDCG over the feature subset 'A' would be 0.86, compared to the overall metric of 0.93.

Subset Drift Prediction Variance

Default long description for subset batch runner.

Subset Drift Precision

The precision test is also popularly referred to as positive predictive parity in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the Precision of model predictions within a specific subset is significantly lower than the model prediction Precision over the entire population.

Why it matters: Having different precision (e.g. false discovery rates) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. Note that positive predictive parity does not necessarily indicate equal opportunity or predictive equality: as a hypothetical example, imagine that a loan qualification classifier flags 100 entries for group A and 100 entries for group B, each with a precision of 100%, but there are 100 actual qualified entries in group A and 9000 in group B. This would indicate disparities in opportunities given to each subgroup.

Configuration: By default, Precision is computed over all predictions/labels. Note that we round predictions to 0/1 to compute precision.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the Precision over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.5.

Subset Drift False Negative Rate

Default long description for subset batch runner.

Subset Drift Mean-Squared-Log Error (MSLE)

Default long description for subset batch runner.

Subset Drift Rank Correlation

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the rank correlation of model predictions within a specific subset is significantly lower than the model prediction rank correlation over the entire population.

Why it matters: Having different rank correlation between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, rank correlation is computed over all predictions/labels.

Example: Suppose we had the following query-document pairs: [[(qid: 1), 'A'], [(qid: 1), 'A'], [(qid: 2), 'B'], [(qid: 2), 'B']], model predictions [2, 1, 1, 2], and true relevance ranks [1,2,1,2]. Then, the rank correlation over the feature subset 'A' would be -1, compared to the overall metric of 0.

Subset Drift Mean-Absolute Error (MAE)

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the MAE of model predictions within a specific subset is significantly higher than the model prediction MAE over the entire population.

Why it matters: Having different mean-absolute error between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, mean-absolute error is computed over all predictions/labels.

Example: Suppose we had data with 2 features: [[0.4, 0.2], [0.5, 0.3], [0.7, 0.5], [0.6, 0.7], [0.8, 0.7]], model predictions [0.3, 0.4, 0.8, 0.8, 0.9], and labels [0.5, 0.5, 1.5, 1.5, 1.5]. Then, the Mean-absolute error over the feature subset (0.0, 0.5] for the first feature would be 0.15, compared to the overall metric of 0.46.

Subset Drift Mean-Squared Error (MSE)

Default long description for subset batch runner.

Subset Drift F1

This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the F1 of model predictions within a specific subset is significantly lower than the model prediction F1 over the entire population.

Why it matters: Having different F1 between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation.

Configuration: By default, F1 is computed over all predictions/labels. Note that we round predictions to 0/1 to compute F1 score.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the F1 over the feature subset value 'cat' would be 0.5, compared to the overall metric of 0.57.

Subset Drift Average Prediction

Default long description for subset batch runner.

Subset Drift Prediction Variance (Positive Labels)

The subset variance test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the variance of model predictions within a specific subset is significantly higher than model prediction variance of the entire population. In this test, the population refers to all data with positive ground-truth labels.

Why it matters: High variance within a feature subset compared to the overall population could mean a few different things, and should be analyzed with other subset performance tests (accuracy, AUC) for a more clear view. In the variance metric over positive/negative labels, this could mean the model is much more uncertain about the given subset. When paired with a decrease in AUC, this implies the model underperforms on this subset.

Configuration: By default, the variance is computed over all predictions with a positive ground-truth label.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]] and model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.48]. Assume the labels are [1, 0, 1, 0, 0, 0].Then the prediction variance for feature column 1, subset 'cat' with positive labels would be 0.04.

Subset Drift False Positive Rate

The false positive error rate test is also popularly referred to as as predictive equality, or equal mis-opportunity in fairness literature. This test checks whether the model performs equally well across a given subset of rows as it does across the whole dataset. The key detail displays the performance difference between the lowest performing subset and the overall population. The test first splits the dataset into various subsets depending on the quantiles of a given feature column. If the feature is categorical, the data is split based on the feature values. We then test whether the false positive rate of model predictions within a specific subset is significantly higher than the model prediction false positive rate over the entire population.

Why it matters: Having different false positive rates (e.g. predictive equality) between different subgroups is an important indicator of performance bias; in general, bias is an important phenomenon in machine learning and not only contains implications for fairness and ethics, but also indicates failures in adequate feature representation and spurious correlation. Unlike demographic parity, this test permits assuming different base label rates but flags differing mistake rates between different subgroups. As an intuitive example, consider the case when the label indicates an undesirable attribute: if predicting whether a person will default on their loan, make sure that for people who didn't default, the rate at which the model incorrectly predicts positive is similar for group A and B.

Configuration: By default, false positive rate is computed over all predictions/labels. Note that we round predictions to 0/1 to compute false positive rate.

Example: Suppose we had data with 2 features: [['cat', 0.2], ['dog', 0.3], ['cat', 0.5], ['dog', 0.7], ['cat', 0.7], ['dog', 0.2]], model predictions [0.3, 0.51, 0.7, 0.49, 0.9, 0.58], and labels [1, 0, 1, 0, 0, 1]. Then, the false positive rate over the feature subset value 'cat' would be 1.0, compared to the overall metric of 0.67.

Subset Drift Average Rank

Default long description for subset batch runner.

Data Poisoning Detection

Label Flipping Detection (Exact Match)

This test detects corrupted data points in the evaluation dataset. It does this by checking for input rows in the evaluation set that are also present in the reference set, but with a different label. This test assumes that the reference set is clean, trusted data and the evaluation set is potentially corrupted.

Why it matters: Malicious actors can tamper with data pipelines by sending mislabeled data points to confuse your model. Detecting poisoning attacks before they affect your model is critical to ensuring model security.

Configuration: By default, this test runs when the "Data Poisoning Detection" test category is selected.

Example: Suppose there was an identical input row in both datasets, with label 0 in the reference set and label 1 in the evaluation set. This test would flag the sample in the evaluation set as being corrupted.