Algorithm - Making Data Mistakes

Making Data Mistakes

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Algorithm

A collection of 4 posts

Make it work, make it right, make it fast

Make it work, make it right, make it fast

I first heard this phrase from Gael Varoquax [http://gael-varoquaux.info/] (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

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

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.

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

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

Wrong answers matter in different ways for a retina scanner outside a bank vault vs a cancer detector. This is why you need to understand precision vs recall.

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.