train_test_split的random_state取值与场景，为何用0而非1？

Hey there! Let's unpack your questions about random_state in train_test_split() clearly.

What Value Should You Pick for random_state? #

First off, random_state controls the random seed used to split your data. The key points here are:

• You can use any integer (positive, negative, zero—doesn’t matter). There’s no "correct" value, but the most critical rule is to stick with the same value throughout your project if you need consistent, reproducible results.
• If you skip setting this parameter entirely, every time you run the function, you’ll get a different train/test split. That’s fine for quick, one-off tests, but not ideal when you need others (or future you) to replicate your work.

Common Scenarios for Using a Fixed random_state #

Here are the main situations where locking in a random_state is essential:

• Reproducible work: Whether you’re sharing code with teammates, publishing research, or revisiting your project months later, a fixed random_state ensures everyone gets exactly the same data split you did.
• Fair model comparisons: When testing different models (like logistic regression vs. a random forest) or tweaking hyperparameters, using the same random_state means all models are evaluated on the exact same train/test data. This eliminates the chance that one model performs better just because it got luckier with the split.
• Debugging: If you’re fixing a bug in your model code, a consistent data split lets you isolate issues—you won’t have to wonder if a weird result came from your code or a random, one-off data split.

Why random_state=0 Instead of random_state=1? #

Short answer: There’s no functional difference—it’s purely a matter of convention and personal preference.

• Both 0 and 1 are arbitrary integer seeds that will produce a fixed, reproducible split. Many tutorials and documentation examples use 0 as a default example, which is why you’ll see it so often.
• The number itself doesn’t matter; what matters is consistency. You could just as easily use 42, 123, or even -7—all will give you a consistent split, just a different one from each other.
• For example, if you use random_state=0 in your initial split, keep using 0 for any related splits (like splitting your training set into train/validation later). Switching to 1 halfway through would create inconsistent splits, which could throw off your model evaluations.

内容的提问来源于stack exchange，提问作者Manas Kumar