Ai+ Training

"Explainability Explained: From Beta Coefficients to SHAPly Values"" is a comprehensive exploration of the concept of explainability in machine learning. Beginning with an overview of the regulatory requirements and the fundamental human need for comprehensible models, the talk demonstrates how the challenges around explainability evolved in the context of machine learning systems.

The talk culminates in a deep dive into SHAPly values as a powerful mechanism for facilitating local explanations. By leveraging SHAPly values, attendees learn how to uncover insights into individual predictions, enhancing their ability to interpret and trust machine learning models in practical applications. Throughout the presentation, attendees have access to a Python Jupyter Notebook containing meticulously crafted examples, enabling hands-on exploration and reinforcing key concepts with real-world implementations. With a wealth of knowledge and practical tools at their disposal, attendees leave equipped to navigate the complex landscape of machine learning explainability with confidence. "