A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
Counterfactual explanations are viewed as an effective way to explain machine learning predictions. This interest is reflected by a relatively young literature with already dozens of algorithms aiming to generate such explanations. These algorithms are focused on finding how features can be modified to change the output classification. However, this …