Help Your Model Learn the True Signal

A new algorithm-agnostic method, inspired by Cook's distance, has been developed to improve the identification of true signals in machine learning models. This approach enhances the robustness of model diagnostics by evaluating the influence of individual data points across various algorithms, facilitating more accurate detection of influential observations and reducing model bias.