Machine Learning Trade-offs
Irene emphasizes the importance of considering trade-offs in machine learning, particularly in clinical settings. She highlights the need for accuracy, addressing both false positives and false negatives, while also stressing the significance of understanding what happens after a model's predictions. The conversation merges insights from clinical expertise and machine learning, focusing on the sources of errors—whether from data variance, measurement noise, or model bias.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Machine Learning for Equitable Healthcare Outcomes with Irene Chen - #479
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