Explaining Neural Network Models with SHAP Values: A Mathematical Perspective | by Kevin Akbari | Jun, 2024 | Medium

In the realm of machine learning, neural networks have emerged as powerful models capable of capturing complex patterns in data. However, their complexity often renders them as “black boxes,” making it challenging to interpret how they make predictions. SHAP (SHapley Additive exPlanations) values provide a solution by offering a unified framework to explain the output of any machine learning model, including neural networks. In this blog, we will delve into the mathematical underpinnings of SHAP values and demonstrate their application in explaining neural network models with Python code. SHAP values are rooted in cooperative game theory, specifically the concept of Shapley values. Given a neural network model f and an input feature vector x, the goal of SHAP is to attribute the contribution of each feature to the model’s output. In cooperative game theory, the Shapley value is a method for distributing the total gains of a coalition to individual players. For a game with N players, th

Member-only story Explaining Neural Network Models with SHAP Values: A Mathematical Perspective Kevin Akbari 4 min read · Jun 27, 2024 -- Listen Share Introduction In the realm of machine learning, neural networks have emerged as powerful models capable of capturing complex patterns in data. However, their complexity often renders them as “black boxes,” making it challenging to interpret how they make predictions. SHAP (SHapley Additive exPlanations) values provide a solution by offering a unified framework to explain the output of any machine learning model, including neural networks. In this

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