Steve Harris

Maybe the nicest explanation I’ve read so far. But then, they should know!

Not just a cool scientific advance, but a very cool explanation of a new technique. For that matter, a lovely explanation of matrix multiplication for the non-mathematician.

New Algorithm Breaks Speed Limit for Solving Linear Equations By harnessing randomness, a new algorithm achieves a fundamentally novel — and faster — way of performing one of the most basic computations in math and computer science.

Quanta Magazine quantamagazine.org

What a cool set of resources including notes on R, make and more!

Visually select the text then gq

I followed and modified the instructions here. This was much easier than trying to make do with the readme.

The key insight here is that you need to leave something running in the foreground.

How do I keep Docker containers running in the background? If you would like to keep your container running in detached mode, you need to run something in the foreground. An easy way to do this is to tail the /dev/null device as the CMD or ENTRYPOINT command of your Docker image. This command could also run as the last step in a custom script used with CMD or ENTRYPOINT .

via source

A simple, step-by-step guide to interpreting decision curve analysis

50 Years of Data Science: Journal of Computational and Graphical Statistics: Vol 26, No 4:

This paper is a great find. Not the least because the argument (statistics versus data science) was already in full swing 50 years ago.

I have no problem with predictive modelling, but it is a different task. And it does seem that the emphasis on ML has obscured the value (in a pendulum swing from the days of Tukey) on importance of understanding the generative model. From Donoho …

Predictive modeling is effectively silent about the underlying mechanism generating the data, and allows for many different predictive algorithms, preferring to discuss only accuracy of prediction made by different algorithm on various datasets. The relatively recent discipline of machine learning, often sitting within computer science departments, is identified by Breiman as the epicenter of the predictive modeling culture.

I like to think that our lab is pulling hard at the pendulum. That we care massively about the underlying mechanism. That for me is the ‘science’ in ‘data science’. Science because when right it tells us something about how the world works, not just how it will be. The difference between a super accurate weather forecast, and understanding the principles of the atmosphere and the climate. None of that devalues predictive modelling, but these are separate activities.