SHAP : A Comprehensive Guide to SHapley Additive exPlanations SHAP (SHapley Additive exPlanations) has a variety of visualization tools that help interpret machine learning model predictions These plots highlight which features are important and also explain how they influence individual or overall model outputs
Shapley value - Wikipedia In cooperative game theory, the Shapley value is a method (solution concept) for fairly distributing the total gains or costs among a group of players who have collaborated For example, in a team project where each member contributed differently, the Shapley value provides a way to determine how much credit or blame each member deserves It was named in honor of Lloyd Shapley, who introduced
SHAP-Integrated Machine Learning Framework for Interpretable Survival . . . The machine-learning model demonstrated robust predictive capability, while SHAP analysis highlighted the dominant impact of age, COVID-19 severity, and comorbid conditions on survival Vaccination status and reinfection emerged as key modifiable predictors The SHAP framework provided patient-level and global insights into risk drivers, facilitating transparent decision-making
Systematic evaluation of peptide property predictors with explainable . . . SHAP generally is hampered by its computational cost, which scales very poorly with large inputs Therefore peptide property predictors that take as input amino acid sequences, roughly between the lengths of 10-30 amino acids, represent ideal systems for applying SHAP
What is SHAP (SHapley Additive exPlanations)? SHAP (SHapley Additive exPlanations) is a game theory approach to explain the output of any machine learning model It connects optimal credit allocation with local explanations using the classic Shapley values from cooperative game theory and their related extensions
SHAP Values Explained - Medium SHAP (SHapley Additive exPlanations) is a powerful tool in the machine learning world that draws its roots from game theory In simple terms, SHAP values allow you to break down a machine
Choose your explanation: a comparison of SHAP and Grad-CAM . . . - Springer While this work is the first to apply SHAP on the primary input features, the direct comparison with the often-used Grad-CAM method within HAR is lacking A comparison between SHAP and Grad-CAM can provide a more complete evaluation of the individual strengths and weaknesses, which is not possible if only one explanation method is used
An in-depth analysis of KernelSHAP and SamplingSHAP: assessing . . . AbstractThe growing importance of explainable artificial intelligence (XAI) has brought SHAP (SHapley Additive exPlanations) to the forefront as one of the most widely adopted model-agnostic explanation frameworks Among its variants, KernelSHAP and