Train/Test Splitting
18 min
train_test_split shuffles the data before splitting it, which means
two calls without a fixed seed produce DIFFERENT train/test sets every
time — making it impossible to reproduce a specific result or fairly
compare "model A vs. model B" (since they'd see different data). The
random_state parameter fixes this: the shuffle becomes fully
deterministic, exactly like random.seed() from Monte Carlo Simulation's
seeded-randomness technique, just scoped to scikit-learn's own random
number generation.
from sklearn.model_selection import train_test_split
def same_test_set(X, y, test_size, seed_a, seed_b):
_, X_test_a, _, _ = train_test_split(X, y, test_size=test_size, random_state=seed_a)
_, X_test_b, _, _ = train_test_split(X, y, test_size=test_size, random_state=seed_b)
return X_test_a == X_test_b
test_size=0.25 controls HOW MANY rows go to the test set; random_state controls WHICH rows -- two completely independent knobs that are easy to conflate when first learning this API.
Write `same_test_set(X, y, test_size, seed_a, seed_b)`: split `(X, y)` twice with `train_test_split`, once using `random_state=seed_a` and once using `random_state=seed_b`. Return `True` if both splits produced the exact same `X_test`, `False` otherwise.
Why does `train_test_split`'s `random_state` parameter matter for reproducibility, given that a train/test split is supposed to be a RANDOM shuffle?
You can use train_test_split with a fixed random_state to produce fully reproducible splits, and understand why reproducibility matters for debugging and fair model comparisons.