Reverse Dependencies of aiverify-test-engine

The following projects have a declared dependency on aiverify-test-engine:

• aiverify-blur-corruptions — Part of AI Verify image corruption toolbox. This package includes algorithms that adds blur corruptions (defocus, gaussian, glass, horizontal motion, vertical motion and zoom Blur) to images at 5 severity levels, to test the robustness of machine learning models.
• aiverify-digital-corruptions — Part of AI Verify image corruption toolbox. This package includes algorithms that add digital corruptions (brightness up and down, contrast up and down, compression, random tilt, saturate) to images at 5 severity levels, to test the robustness of machine learning models.
• aiverify-environment-corruptions — Part of AI Verify image corruption toolbox. This package includes algorithms that add environmental corruptions (rain, fog and snow) to images at 5 severity levels, to test the robustness of machine learning models.
• aiverify-fairness-metrics-toolbox-for-classification — AI Verify Fairness Metrics Toolbox (FMT) for Classification contains a list of fairness metrics to measure how resources (e.g. opportunities, food, loan, medical help) are allocated among the demographic groups (e.g. married male, married female) given a set of sensitive feature(s) (e.g. gender, marital status). This plugin is developed for classification models.
• aiverify-fairness-metrics-toolbox-for-regression — AI Verify Fairness Metrics Toolbox (FMT) for Regression contains a list of fairness metrics used to measure how resources (e.g. opportunities, food, loan, medical help) are allocated among the demographic groups (e.g. married male, married female) given a set of sensitive feature(s) (e.g. gender, marital status). This plugin is developed for regression models.
• aiverify-general-corruptions — Part of AI Verify image corruption toolbox. This package includes algorithms that add general corruptions (gaussian, poisson and salt and pepper noise) to images at 5 severity levels, to test the robustness of machine learning models.
• aiverify-partial-dependence-plot — AI Verify implementation of Partial Dependence Plot (PDP) that explains how each feature and its feature value contribute to the predictions.
• aiverify-robustness-toolbox — AI Verify Robustness Toolbox generates a perturbed dataset using boundary attack algorithm on the test dataset.
• aiverify-shap-toolbox — AI Verify SHAP Toolbox provides SHAP (SHapley Additive exPlanations) methods to explain the output of machine learning models.