Dillon Gardner Presents:
AUC is Worthless: Lessons in Transitioning From Academic to Business Data Science
New data scientists often struggle to make major impacts on solving business problems despite impressive technical skills. A core challenge is the gap between how academics think about performance of models and what matters for a company. As an example, academic work summarizes a model’s receiver operator characteristic (ROC) curve with the area under the curve (AUC). This summary statistic is useless for business applications, which will always have unique trade-offs and constraints. Effective approaches to optimize model performance requires understanding the specific business requirements and how to map that to a well framed data science problem.
In this talk, I will go through a framework of how to think effectively about model trade-offs in terms of maximizing business utility. Through this exercise, we will build intuition for what is required for a model in production to be a success and how to collaborate more effectively with non-technical co-workers.
Slides: https://pydata.org/london2022/wp-content/uploads/2022/07/AUC_is_Worthless_DR_Gardner.pdf
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