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Hands-on Introduction to Interpreting Machine Learning Models


Interpretable machine learning is needed because machine learning by itself is incomplete as a solution. The complex problems we solve with machine learning can’t be solved with traditional software development precisely because we don’t understand all of the problem space we’re trying to solve. By explaining a model’s decisions, we can cover gaps in our understanding of the problem, and increase trust in the products we build with AI. In this session, we will cover the importance of model interpretation and explain various methods and their classifications, including feature importance, feature summary, and local explanations.

Serg Masis
Serg Masis

Data Scientist and Author

Serg Masis is a data scientist in agriculture with a lengthy background in entrepreneurship and web/app development, and the author of the bestselling book “Interpretable Machine Learning with Python”, the second edition soon to be released. Passionate about machine learning interpretability, responsible AI, behavioral economics, and causal inference.

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