Linear Digressions

You may be shocked to hear this, but sometimes, people on the internet can be mean.  For some of us this is just a minor annoyance, but if you're a maintainer or contributor of a large project like Wi...

13 Mar 201715min

Advances in neural networks are moving fast enough that, even though it seems like we talk about them all the time around here, it also always seems like we're barely keeping up.  So this week we have...

6 Mar 201716min

When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you ...

27 Feb 201727min

Say you're looking to use some Bayesian methods to estimate parameters of a system. You've got the normalization figured out, and the likelihood, but the prior... what should you use for a prior? Em...

20 Feb 201718min

Have you been out protesting lately, or watching the protests, and wondered how much effect they might have on lawmakers? It's a tricky question to answer, since usually we need randomly distributed ...

13 Feb 201716min

Remember last week, when we were talking about how great the ROC curve is for evaluating models? How things change... This week, we're exploring calibrated risk models, because that's a kind of mode...

6 Feb 201714min

This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

30 Jan 201715min

If one machine learning model is good, are two models better? In a lot of cases, the answer is yes. If you build many ok models, and then bring them all together and use them in combination to make ...

23 Jan 201713min