9.6 SHAP (SHapley Additive exPlanations)

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.

9.6 SHAP (SHapley Additive exPlanations)

Explaining Machine Learning Models: A Non-Technical Guide to

The Pitfalls of Mining for QuantiFERON Gold in Severely Ill Patients With COVID-19 - ScienceDirect

9.3 Counterfactual Explanations

Chapter 6 Model-Agnostic Methods

8 Shapley Additive Explanations (SHAP) for Average Attributions

From local explanations to global understanding with explainable

Application of explainable artificial intelligence in the identification of Squamous Cell Carcinoma biomarkers - ScienceDirect

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

GitHub - shaoshanglqy/shap-shapley

Extracting spatial effects from machine learning model using local

PDF) Data-centric explainability and generating complex stories as explanations from machine learning models

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

How to Easily Customize SHAP Plots in Python, by Leonie Monigatti

Welcome to the SHAP documentation — SHAP latest documentation

Explainable AI, LIME & SHAP for Model Interpretability, Unlocking AI's Decision-Making

A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys

13,441 Apple Body Shape Images, Stock Photos, 3D objects, & Vectors