As machine learning models become increasingly complex and ubiquitous, the need for explainability and interpretability has emerged as a critical challenge. Explainable Machine Learning aims to bridge the gap between the opaque decisions made by these models and the human understanding of their reasoning processes. One powerful tool in the arsenal is the concept of Shapley values, derived from cooperative game theory.
This lecture will be about applying Shapley values in the context of explainable machine learning. Shapley values provide a principled approach to quantifying the contribution of each feature to a model's prediction, shedding light on the decision-making process. By breaking down the model's output into interpretable components, Shapley values enable us to understand the relative importance of different features and their interactions and consider practical examples.
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