Introduction

random_state in scikit-learn is not a magic performance knob. It controls pseudo-randomness so that shuffling, initialization, bootstrapping, and other randomized steps can be reproduced or intentionally varied.

What random_state Controls

Many scikit-learn objects contain randomness somewhere in their workflow. Examples include:

• 'train_test_split() when shuffling data'
• 'RandomForestClassifier when sampling rows and features'
• 'KMeans when choosing initial centroids'
• 'SGDClassifier when shuffling training examples'
• randomized cross-validation splitters such as ShuffleSplit

When you pass random_state, you decide how that randomness is seeded. In practice you will usually use one of three forms:

• 'random_state=None'
• 'random_state=42 or any other integer'
• 'random_state=np.random.RandomState(...)'

Those choices are not equivalent.

The Simplest Rule: Use an Integer for Reproducibility

If you want the same result every time you rerun the script, pass an integer:

1from sklearn.model_selection import train_test_split
2from sklearn.ensemble import RandomForestClassifier
3
4X = [[0, 1], [1, 0], [0, 0], [1, 1], [2, 2], [2, 1]]
5y = [0, 0, 1, 1, 1, 0]
6
7X_train, X_test, y_train, y_test = train_test_split(
8    X, y, test_size=0.33, random_state=42
9)
10
11model = RandomForestClassifier(n_estimators=50, random_state=42)
12model.fit(X_train, y_train)
13print(model.score(X_test, y_test))

Using an integer means repeated calls with the same code and data will produce the same split and the same model randomness. This is ideal for debugging, tutorials, CI, and team collaboration.

Why None Gives Different Results

If you leave random_state=None, scikit-learn uses a changing random source. That is fine when you do not care about exact reproducibility, but it means two runs of the same notebook can give slightly different scores.

That is not inherently bad. In fact, it can be useful when you want to see whether your result is stable. If a model only looks good with one lucky seed, the pipeline may be fragile.

Integer vs RandomState Instance

The scikit-learn documentation points out a subtle difference here. Passing an integer resets the random generator in a reproducible way for that estimator or splitter. Passing a RandomState instance shares a mutable generator object.

Example:

1import numpy as np
2from sklearn.ensemble import RandomForestClassifier
3
4rng = np.random.RandomState(0)
5
6model_a = RandomForestClassifier(random_state=rng)
7model_b = RandomForestClassifier(random_state=rng)

Both models now depend on the same evolving generator. Fitting one model changes the generator state seen by the other. That can be useful in advanced experiments, but it is often surprising.

For most day-to-day work:

• use an integer for deterministic experiments
• use None or multiple different integers when testing robustness
• avoid sharing one RandomState instance unless you truly want coupled randomness

Do Not Tune the Seed as a Hyperparameter

One common mistake is trying several seeds and keeping the one with the best validation score. That is usually data snooping, not real model improvement. The seed changes the random path, not the underlying model quality.

If results vary a lot across seeds, the better response is to improve the evaluation process:

• use cross-validation
• increase dataset size if possible
• simplify the model
• report average and spread across repeated runs

Here is a small example using repeated evaluation:

1from sklearn.datasets import make_classification
2from sklearn.model_selection import train_test_split
3from sklearn.ensemble import RandomForestClassifier
4
5X, y = make_classification(n_samples=500, n_features=10, random_state=0)
6
7scores = []
8for seed in [0, 1, 2, 3, 4]:
9    X_train, X_test, y_train, y_test = train_test_split(
10        X, y, test_size=0.25, random_state=seed
11    )
12    model = RandomForestClassifier(n_estimators=100, random_state=seed)
13    model.fit(X_train, y_train)
14    scores.append(model.score(X_test, y_test))
15
16print(scores)
17print(sum(scores) / len(scores))

This gives a better sense of stability than locking onto one "lucky" value.

Splitters and Estimators Are Slightly Different

Scikit-learn's guidance is more nuanced than "always use 42." For cross-validation splitters, passing an integer is usually the safest way to make repeated calls reproducible. For estimators, an integer is also fine when exact reproducibility matters, but it can hide variability that would appear under different random draws.

That means you should pick the seed based on the goal:

• debugging: fixed integer
• publication or internal report: fixed integer plus note the version and setup
• robustness check: several different seeds
• production training: fixed seed for traceability, unless your team has a reason not to

Common Pitfalls

• Treating 42 as special. Any integer seed is fine; the value itself has no magic.
• Using different seeds for train/test split and model fitting without documenting them makes runs harder to reproduce.
• Sharing one RandomState instance across many objects can create subtle dependencies.
• Tuning the seed to get a better score is not legitimate model selection.
• Forgetting that some estimators are deterministic and ignore random_state entirely.

Summary

• 'random_state controls reproducibility, not model intelligence.'
• Use an integer when you want repeatable results across runs.
• Use multiple seeds when you want to measure stability rather than freeze one outcome.
• Do not optimize the seed as if it were a real hyperparameter.
• Be aware that an integer, None, and a shared RandomState instance behave differently.