Lost in Interpretability

The Center for Data Science (CDS) at NYU has a weekly lunch seminar series. Each Monday, one speaker gives an (informal) presentation on any topic she/he wants to talk about, or at least so I thought. Anyways, I thought it would be a good chance to discuss with people (students, research fellows at CDS as well as faculty members from various departments all over NYU) what the interpretability of machine learning models means. I prepared a set of slides based on an excellent article <Statistical Modeling: The Two Cultures> by Leo Breiman.

Instead of trying to write what I’ve talked about here, I’ll put a link to my slides:

https://drive.google.com/file/d/0B16RwCMQqrtdeTIyUl84SjA3UEE/view?usp=sharing

Oh, right, there were a lot of wonderful discussions on this topic during and after the talk. 

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