Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but oftentimes new data scientists only get experience with a few kinds of metrics when they’re learning and those metrics have real shortcomings when you think about what they tell you, or don’t, about how well you’re really solving the underlying problem. This episode takes a step back and says, what are some metrics that are popular with data scientists, why are they popular, and what are their shortcomings when it comes to the real world? There’s been a lot of great thinking and writing recently on this topic, and we cover a lot of that discussion along with some perspective of our own.

Relevant links:
https://www.fast.ai/2019/09/24/metrics/
https://arxiv.org/abs/1909.12475
https://medium.com/shoprunner/evaluating-classification-models-1-ff0730801f17
https://hbr.org/2019/09/dont-let-metrics-undermine-your-business

Data scientists: beware of simple metrics

Linear Digressions