Data as of Aug 16, 2026 · Based on 3,131,739 AI responses across 10,525 prompts · See how Parse measures this
SHAPshap.readthedocs.ioSHAP (SHapley Additive exPlanations) is a game-theoretic framework that explains the output of any machine learning model by connecting optimal credit allocation with local explanations using Shapley values. It provides tools for interpreting model predictions across tabular, text, image, and genomic data through various explainers and visualization methods.
Sources
shap.readthedocs.io shapes more of what AI says about SHAP than any other source, at 20% of its citations.
medium.com · github.com · researchgate.net · aibucket.io
The market map
Explainable AI & Model Monitoring Platforms →Excerpts where SHAP appeared in the AI's answer
SHAP is the best underlying explanation method for most tabular ML models.
Excerpts where SHAP appeared in the AI's answer
SHAP (SHapley Additive exPlanations) + LIME - Best for: Underlying mathematical feature attribution
SHAP is probably the strongest choice if you want highly detailed, model-agnostic feature-contribution explanations
