Toronto Data Science Group – One or the Other: An Overview of Binary Classification Methods

So Chris was kind enough to invite me to speak at the Toronto Data Science Group again this past Thursday. I spoke on binary classification, and made an effort to cover a fair bit of ground and some technical detail, while still making it accessible. I wanted to give an overview for an audience that was more interested in the ‘how’, and the practical realities of using classification to solve problems within an organization.

As before, I’ll keep my observations to be more about presenting and less about the content.

The meetup is a lot different now, having presentations at venues like MaRS or the conference room at Thompson Hotel with large audiences, as opposed to the early days when it was much smaller.

Speaking to a larger group is challenging; both in that it’s more nerve-racking, and I also noticed it was harder to make eye contact and include the whole audience than I am used to with smaller groups. The temptation is to just look out straight ahead in front of you. Speaking in front of a podium has its disadvantages this way, but it does keep you anchored and give you something on which to rest your hands and remain centered. Looking back toward the screen is usually a bad idea when presenting regardless of audience size, unless you are pointing something out, and is doubly so when that screen is very large and above you.

Some folks were kind enough to take some photos of me during the talk for social media and the like. In retrospect, while I do try to have a very visual style (and inject some humour with it) I think it can come across as overly simplistic and flippant in certain contexts, such as with this larger group. There’s a balance to be struck there, I’m sure. Also, as always, you need to be mindful of how large you are making things on your slides (especially text), given the size of the screen with respect to the venue.

The point I made about the explainability of different classification methods to the non-technical audience or end consumer (i.e. client) receiving the results of their application was less controversial than I would have thought. Chris commented on this as well.

As always I was overly ambitious and was able to get through a lot less material in the timeframe than I originally would have thought.

I was asked some very insightful and detailed questions, some of which I wasn’t totally prepared to answer. Talking about something is fairly easy, I think, because you can put together exactly what you want to say and rehearse; it’s in the answering of the questions that people decide whether you really know the subject, or just putting pretty pictures up on the screen and painting in broad verbal strokes. Many people in the audience seemed to have assumed that because I was speaking on the topic of binary classification that I was a complete expert on it – there’s a danger here too, I think, when you see anyone give a presentation.

All in all, I think the talk was very well received. As always I learned a lot putting it together, and even more afterward, discussing with Toronto’s data scientists and knowledgeable analysts with insightful points of view.

Looking forward to the next one.