shap and shap-analysis-guide
The first is a core library implementing SHAP explainability methods, while the second is a non-technical interpretive guide for understanding SHAP outputs—making them complements where the guide helps users apply the library's results.
About shap
shap/shap
A game theoretic approach to explain the output of any machine learning model.
This tool helps data scientists and machine learning engineers understand why their machine learning models make specific predictions. By taking a trained model and input data, it shows how much each individual feature contributes to the final output, clarifying complex model behavior. It's designed for anyone building or using ML models who needs to explain their results, like a business analyst evaluating a credit risk model or a medical researcher interpreting a diagnostic tool.
About shap-analysis-guide
AidanCooper/shap-analysis-guide
How to Interpret SHAP Analyses: A Non-Technical Guide
This guide helps business leaders and decision-makers understand why a machine learning model makes certain predictions. It takes the detailed outputs from a SHAP analysis, which shows how different factors influence a model's decision, and translates them into clear, actionable insights for non-technical audiences. It's designed for anyone who relies on machine learning models but isn't a data scientist.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work