SHAP & LIME for Binary Classification | Explainable AI with Breast Cancer Dataset

🎓 In our Explainable AI tutorial series, we dive into hands-on coding with SHAP and LIME using the Breast Cancer Wisconsin dataset. Watch as we apply these powerful tools in Python with Jupyter Notebook to interpret model predictions in a binary classification task. 🔍 What You'll Learn: 00:00 - Introduction to the Breast Cancer Classification Problem 02:33 - Dataset Overview (Breast Cancer Wisconsin) 03:00 - 11:00 - Exploratory Data Analysis (EDA) 15:40 - Training Binary Classifiers: Random Forest & XGBoost 18:00 - 26:00 - Global Explanation: Feature Importance Scores 30:00 - Post-hoc Model Interpretations: SHAP and LIME 43:00 - Real-World Interpretability Insights 👩‍💻 Tools: Python, Jupyter Notebook, scikit-learn, SHAP, LIME 📁 Download the Jupyter Notebook: 👉 https://github.com/Donmaston09/Learning-Machine-Learning-with-Onoja/blob/main/LIME_Explainable_AI_Part_2.ipynb 📌 Perfect for data science practitioners, enthusiasts, students, and biomedical experts looking to build more transparent and trustworthy machine learning models. 👍 Don’t forget to Like, Share, and Subscribe for more exciting content like this! #explainableai #shap #lime #pythontutorial #jupyternotebook #breastcancer #machinelearning #XAI #ModelInterpretability #datascience #wisconsin #education #science #academicexcellence #artificialintelligence #ai #featureselection #dataanalysis #learningai #project #insights #explanations #learningprogress #studentsuccess #tech #chatgpt #biomedical #biomedicalscientist #africanyouths #nigeria #usa #viralvideo