KC
Current MBA student at Kellogg School of Management. Previously an Associate at Boston Consulting Group, @TorstarDigital
Interpretable models can be understood by less-technical (non data scientists) in the business and, importantly, they are often the decision makers. They need to understand and use the model, otherwise the work has no ultimate impact. My job as a data scientist is to maximize impact.
The role of model interpretability in data science
Carl Anderson
232
When a model is understood by a less-technical team, it also allows them to add insight based on their knowledge that can ultimately help the data scientist create a better model. Human intuition for a problem is built up by experience and knowledge of that problem space — that intuition can be very powerful for building strong models.
