627: AutoML: Automated Machine Learning
Super Data Science: ML & AI Podcast with Jon Krohn
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Admissible Machine Learning and Fairness
Exploring the concept of admissible machine learning introduced in a recent paper, focusing on fairness in machine learning through the calculation of conditional mutual information. The chapter delves into developing a metric to measure and mitigate the impact of sensitive variables on model outcomes, emphasizing the importance of creating fairer models by addressing information leakage and utilizing tools like the H2O infogram.
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