Uploaded April 2022 | Updated September 2026, 2 weeks ago

---

This video explains how to calculate a Shapley value with a very simple example. The Shap calculation based on three data features only to make this example as simple as possible. Also, you will be introduced to a main Shapley value formula, where we will calculate weights and marginal contributions.

The Shapley value is a solution concept used in game theory that involves fairly distributing both gains and costs to several actors working in coalition. Game theory is when two or more players or factors are involved in a strategy to achieve a desired outcome or payoff.

In many cases, the Shapley value can be replace a standard feature importance calculations provided by Scikit-learn, because the Shapley value can explain how the feature's values can impact the final prediction, and by how much they contributes to the Machine Learning predictions.

The video explains the Shapley value calculation by involving you to a simple example, where you analyze how Google Ads, Social Media, and Email marketing contributes. to prediction - if a user click on Ad, or not.

Shap value also ofter used in Explainable AI (XAI) to explain results to the business and understand the reasonings behind the ML models in terms of predictions and model performance.

The content of the video:
0:00 - What is a Shapley value and how to explain it?
2:14 - Simple Shapley value calculation example
3:00 - Understand Marginal Contributions for Shap
6:35 - Shap formula
7:13 - Calculate Marginal Contributions for Shap
11:20 - Calculate Weights for Marginal Contributions
13:31 - Calculate the final Shapley value
14:17 - Final summary of calculation results

Read more:
- Official Shap documentation: shap.readthedocs.io/en/latest/index.html
- Medium post: SHAP: Explain Any Machine Learning Model in Python (towardsdatascience.com/shap-explain-any-machine-learning-model-in-python-24207127cad7)

#shap #explainableAI #shapley