parametersets

A parameter set is a defined collection of parameter values that specify the configuration of a model, simulation, experiment, or system. Each parameter in a set has a name, a value, and often units, ranges, and constraints. A parameter set serves as a snapshot of a model’s choices at a given moment, enabling repeatability and comparison across variants.

Parameter sets are widely used across disciplines such as statistics, machine learning, engineering, and scientific computing. In statistics, a parameter set may refer to a vector of model parameters estimated from data. In machine learning, hyperparameter sets define architecture choices, learning rates, regularization, and other training options; multiple sets are explored during optimization or cross-validation. In simulations and engineering, parameter sets represent initial conditions, physical constants, or control settings used to run experiments and compare outcomes.

Storage and management: parameter sets are typically stored in configuration files or data structures, and are often serialized as JSON, YAML, or XML. They may include metadata such as version, source, author, and provenance. Sets can be nested or layered, with base configurations and delta updates, supporting templating and reuse.

Creation methods: parameter sets can be specified manually, derived from templates, or generated through automated search methods such as grid search, random search, or Bayesian optimization. They enable systematic exploration of model behavior, sensitivity analysis, scenario planning, and parameter sweeps.

Reproducibility and interoperability: fixing a parameter set and recording its provenance enhances reproducibility and facilitates exchange between tools that consume configuration data.

See also: parameterization, hyperparameters, configuration management, experiments, reproducibility.