Why is everyone in Machine Learning obsessed with the number 42?

If you work in Machine Learning, Deep Learning, or general programming, you’ve likely encountered it random_state=42 everywhere.

Some beginners believe it is a "lucky" seed that boosts model accuracy. Others suspect it’s a mathematical constant optimized for data splitting. The truth? It has nothing to do with better accuracy, superior randomness, or a more "scientific" split.

The Origin Story 

The reason people choose 42 comes from Douglas Adams’ The Hitchhiker’s Guide to the Galaxy. In the story, a supercomputer named Deep Thought is built to find the “Ultimate Answer to Life, the Universe, and Everything.” After 7.5 million years of computation, the answer it provides is simply: 42. The joke is that while the answer is definitive, nobody actually knows what the question was, making the result both profound-looking and practically useless.

How it Became a Standard 

This joke became a staple of programmer and sci-fi culture. When developers needed an arbitrary seed for their code, many picked 42 as a playful reference. Over time, tutorials, blog posts, and official documentation (like Scikit-learn) reused it so often that it became an industry convention. While Scikit-learn also uses seeds like 0 or 1, the "magic" of 42 stuck.

The funniest part? Douglas Adams himself confirmed there was no hidden meaning. He rejected theories about binary code, base-13, or mysticism. His explanation was simple: It had to be an ordinary, smallish number, and 42 just worked.

• ML Reason: Any fixed integer seed ensures reproducibility. It doesn't have to be 42; any number will do.

• Culture Reason: 42 is a long-running tribute to The Hitchhiker’s Guide to the Galaxy.

• Scientific Reason: None.

• Best Practice: Use a fixed seed to keep your results consistent, but always test your model across several different seeds to ensure your high accuracy isn't just a "lucky" split!