The Y Combinator Explained in Python.

  Nick Cave & The bad Seeds, The Abattoir Blues Tour.

Why linear mixed-effects models are probably not the solution to your missing data problems. #rstats

On Moving from Statistics to Machine Learning, the Final Stage of Grief.

Slowly going back to my unread feeds in Reeder.app. Today is After WWDC 2020: bittersweet Mac. #apple

Logistic regression is not fundamentally a classification algorithm.

The problem arises from logistic regression often being taught as a “classification” algorithm in the machine learning world. I was personally not taught this way– I learned from econometricians that you can use either probit or “logit” as general linear models in the event you want to estimate on a binary target variable, and that these models calculate probabilities. Thinking about logistic regression as a probability model easily translates to the classification case, but the reverse simply does not seem to be true. — Why Do So Many Practicing Data Scientists Not Understand Logistic Regression?

C Needs Better Syntax and Macros. #clang

Rash: The Reckless Racket Shell. #racket

These days I mostly use Emacs on my laptop. Sure, there’s all that stuff in the background. But in terms of applications I actually launch, I’d say I mostly use just Emacs. — Emacs everything

Same here.

There is commonality shared among all, or at least most, of these programming languages, and the Lisp hackers recognize and appreciate that commonality – the characteristics that make a programming language a Lisp. Homoiconic syntax, powerful metaprogramming facilities, and editor support that, in my opinion, is unparalleled. — The Many Faces of an Undying Programming Language