Simplifying Model Interpretability
Tim emphasizes the importance of starting with simple models and understanding feature dependence to avoid misleading interpretations in machine learning. He warns against confusing correlation with dependence and highlights the risks of neglecting feature interactions.In this clip
From this podcast
Machine Learning Street Talk (MLST)
047 Interpretable Machine Learning - Christoph Molnar
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