Randomstate

Randomstate refers to the internal state of a pseudo-random number generator and the sequence of numbers it will produce. By seeding the generator, the sequence becomes reproducible: the same seed yields the same sequence on every run, which supports repeatability in experiments, simulations, and model training. A random state can be customized with different seeds or inputs, depending on the library, to control randomness across processes.

In NumPy, RandomState is a legacy class that implements an older pseudo-random number generator. An instance can be created with a seed and used to generate random numbers or to sample from distributions. It also provides means to retrieve and restore its internal state. Although functional, RandomState is considered deprecated in favor of the newer Generator API, which is accessed via numpy.random.default_rng(seed) and offers improved performance and capabilities.

Many machine learning and data analysis libraries expose a random_state (or random_state) parameter to ensure reproducibility. Supplying an integer seed or a RandomState object can fix the randomness in steps such as data splitting, shuffling, or bootstrapping, so results are repeatable when the same code is executed. If random_state is None, operations typically use a non-deterministic seed, producing different results on each run.

Security and cryptography: a randomstate is not intended to be cryptographically secure. For security-critical applications, cryptographic random number generators should be used instead.

Overall, randomstate is a practical concept for controlling randomness in computational workflows, with different implementations and APIs across libraries, and a shift toward modern generators in current projects.