Evaluators are the classes that perform specific functions on a model and/or data. These can include assessing the model for fairness, or profiling a data. Evaluators are constantly being added to the framework, which creates Lens’s standard library.
Library of Evaluators
This evaluator runs the pandas profiler on a data. Pandas profiler calculates a number of descriptive statistics about the data.
This evaluator assesses whether model outcomes (i.e., predictions) are distributed equally across a sensitive feature. Depending on the kind of outcome, different tests will be performed.
Discrete: chi-squared contingency tests, followed by bonferronni corrected posthoc chi-sq tests
Continuous: One-way ANOVA, followed by Tukey HSD posthoc tests
- Proportion (Bounded [0-1] continuous outcome): outcome is transformed to logits, then
proceed as normal for continuous
This evaluator calculates performance metrics disaggregated by a sensitive feature, as well as evaluating the parity of those metrics.
Handles any metric that can be calculated on a set of ground truth labels and predictions, e.g., binary classification, multiclass classification, regression.
This evaluator calculates overall performance metrics. Handles any metric that can be calculated on a set of ground truth labels and predictions, e.g., binary classification, multiclass classification, regression.