34. Random Forest Classification | Breast | Morries Sensitivity Method | Notebook | Python
en el canal de: Kishan Tongrao
https://github.com/thetongs/xia-via-i...
Welcome to 'XAI Explainable AI with InterpretML | Notebooks | Python'! We’re thrilled to have you join us on this exciting journey into the world of Explainable AI (XAI). This course is designed to empower you with the skills to make machine learning models transparent and interpretable using Python and InterpretML. Whether you’re a beginner or a seasoned data scientist, you’ll dive into practical techniques like Linear Models, Tree-based Models, EBR, ShapKernel, LimeTabular, and more—all within Google Colab. Expect hands-on examples, real-world applications, and a deep understanding of tools like Partial Dependence and SHAP Tree. Our goal? To help you unlock the power of XAI, boost your confidence in model interpretation, and enhance decision-making. Get ready to explore, learn, and transform how you approach AI. Let’s make AI explainable together—your adventure starts now!
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