SHAP tells you why a prediction was made. But then what?

📰 Medium · Machine Learning

Learn how to use SHAP for global feature attribution and understand the next steps after getting feature attribution explanations

intermediate Published 17 May 2026

Action Steps

Use SHAP to calculate global feature attribution for your model
Analyze the SHAP values to understand how each feature contributes to the model's predictions
Identify the most important features driving the model's predictions
Use the feature attribution explanations to inform model improvements or feature engineering
Evaluate the impact of feature attribution on model performance and fairness

Who Needs to Know This

Data scientists and machine learning engineers can benefit from using SHAP to explain their models' predictions, improving model transparency and trustworthiness

Key Insight

💡 SHAP provides global feature attribution, helping you understand how each feature contributes to your model's predictions