Model Interpretability
Tim and Sanyam discuss the importance of interpretability in models post-accuracy era, emphasizing the need for understanding model behavior and avoiding discrimination. They touch on the significance of fairness metrics and the evolving role of AutoML in the data science landscape.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Kaggle, ML Community / Engineering (Sanyam Bhutani)
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