Unlock the power of model interpretability with this hands-on session focused on SHAP (SHapley Additive exPlanations)!
This tutorial uses the Wisconsin Breast Cancer dataset to demonstrate how SHAP can help you understand and explain machine learning model predictions.

Chapters

0:00 Intro
5:00 How to load and prepare the Wisconsin Breast Cancer dataset

15:00 How to build a Random Forest classifier

21:00 How to apply SHAP to interpret model predictions

25:00 How to create SHAP summary plots, dependence plots, and waterfall plots

28:00 Best practices for explaining complex models with SHAP

Whether you are a beginner or an experienced data scientist, this session will strengthen your understanding of model interpretability, an essential skill for trustworthy AI development!

📌 Tools & Libraries Used:

Python

Scikit-learn

SHAP

Matplotlib

Pandas

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🔗 Resources:
github.com/Donmaston09/Learning-Machine-Learning-w…

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