We demo a script that converts python numerical commands to LaTeX format. A notebook available on our GitHub page will take this and pretty print the result.

In my last post, I looked at the gap that arises when we delegate parts of our thought processes to algorithmic models, rather than incorporating the rules they identify directly into our mental models, like we do with traditional statistics. I described how the the idea of model interpretability can make the delegation process smoother by helping to break down the barriers between algorithmic and mental models. An increasing number of research papers these days claim to describe models that are interpretable, or ways of adding a layer of interpretability to existing models, but most of them rely on an implicit, intuitive definition of interpretability, usually one that suits their particular results. It would be nice if there was a canonical notion of what interpretability means. In the next few posts, I plan to explore what such a definition might look like and how you might tell whether a given algorithm is interpretable. In this post, I’ll explore this question from the angle of goals: What should an interpretable model allow you to do that a non-interpretable model can’t.

To see the visualization, click here.

Task

I made my first resume in tenth grade, when I applied to Staples because my Dad wouldn’t buy me the new Assassin’s Creed game. That was the worst recruiting experience of my life: my resume was a shitty template stolen from Microsoft Word, and I was so nervous during the interview that I couldn’t talk without stuttering. After that, I went home and swore that I’d do a PhD after college so I wouldn’t have to interview ever again (oh, how very naive I was).

The hosts of Becoming a Data Scientist podcast, Partially Derivative podcast, Adversarial Learning podcast, and some other awesome data people that do elections forecasting for their day jobs joined together for this talk about the US election and the subsequent major questions surrounding the predictions, since basically all of them heavily leaned toward a different overall outcome than we got. If you’re interested at all in data science surrounding political campaigns, this episode is a must-listen!

A (not too hard) coffee time puzzle this week. Go grab a pencil, a cup of coffee, and dust off a little geometery. ||To the left is a unit square.A semi-circle is constructed inside the square with a diameter of one unit.The yellow line starts at the top corner, and is tangent to the semi-circle.What is the length of the yellow line?|