Algorithm - Making Data Mistakes

Make it work, make it right, make it fast

I first heard this phrase from Gael Varoquax (one of the core scikit-learn committers) in a talk[1], and I loved the super-concentrated wisdom of it: Make it work. First of all, get it to compile, get it to run, make sure it spits out roughly the right kind of

Data Science

How Humans and Machine Learning Deliver the Best Result (Podcast)

I was recently honoured to be a guest on The GrayMeta Podcast - Metadata Matters. In this podcast, I have the opportunity to discuss my perspective on Metadata and how to structure human teams to effectively work with data generated by machine learning. Hosted by the incredibly talented Matt Eaton.

Classification

What is precision vs recall, and why should I care?

What do these tasks all have in common? Looking at an X-ray and deciding whether someone’s leg is brokenScreening a CV for suitability for a job postIdentifying faces of terrorists in a crowdThey are all examples of classification tasks, where we need to make a binary yes/no decision.

Data Science

How to know if your recommendations algorithm is actually doing a good job

I led the team that built Channel 4’s recommender system for All 4 in 2016. It started out as a straightforward project. But after getting lost in a rabbit hole trying to devise a score for ‘provocativeness’ and ‘serendipity’, I learned the single most important lesson about data science.