Adding Global Actions in A Nutshell For Counterfactual Explainability (GLANCE) framework #196
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Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
-Changed Counterfactual Treeto T_GLANCE -Fixed class imports Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
Signed-off-by: Nikolas Theologitis <[email protected]>
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This PR is to add GLANCE, Global Actions in A Nutshell For Counterfactual Explainability framework.
GLANCE is a versatile and adaptive framework for generating global counterfactual explanations. These explanations are expressed as actions that offer recourse to large population subgroups. The framework aims to provide explanations and insights, ensuring that the actions benefit as many individuals as possible.
GLANCE comprises of two algorithms:
C-GLANCE: employs a clustering-based approach for generating global counterfactual explanations that allows the user to specify the number of global actions generated and serves as a tool to explain and debug ML models.
T-GLANCE: generates global counterfactual explanations using a decision tree-like structure. T-GLANCE's key strength is that allows policy-makers or auditors to select the features that determine the partitioning of the tree. By allowing
users to guide the feature selection, T-GLANCE can adapt to specific policy needs or audit requirements.
algorithm folders are here
example notebook can be found here
algorithm's dependencies are here
Closes #195 issue.