Explaining the unexplainable Part I: LIME

After a long break, Duygu Dagli and I have written a new article at Data Duets: Explaining the unexplainable Part I: LIME. This post is about the interpretability of predictive models, explains LIME and discusses its pros and cons.

Why the break? We started this project as an experiment. There were already resources out there on the topics we were discussing. We started by offering two perspectives on one topic: Academic vs. Director.

That was well received but it was not enough for us to focus. After getting some feedback, we scoped the project to focus on what we call data centricity: How can we use models to make data-informed decisions while staying faithful to the actual data?

Now we have two goals: 1) provide academic and practitioner perspectives on the same data science and AI topic/concept, and 2) discuss the implications for business decision making and data centricity.

We have added a section on data centricity to each of our previous posts. You can see an example for causal inference using synthetic controls here. We are excited about this new direction and have more to come. See our latest post on LIME here.