modelinputs

Model inputs are the parameters, data, and assumptions supplied to a computational model to generate its outputs. They form the input layer of the modeling process and are distinguished from model outputs and state variables produced by the model itself. The term is used across disciplines including statistics, machine learning, simulation, and optimization, and the exact composition of inputs depends on the model type.

Typical inputs include data inputs (raw data, features derived from observations), parameter inputs (coefficients, constants), initial and boundary conditions, control or scenario variables, and stochastic inputs such as probability distributions for random processes. Inputs may be numerical, categorical, or temporal, and are commonly organized as arrays, tables, or feature vectors with associated metadata such as units and data provenance.

Role and use: Inputs define model behavior, enable calibration and tuning, and drive analyses such as forecasting, optimization, and scenario testing. Examples include climate models using temperature and greenhouse gas concentrations, financial models using interest rates and volatility, and machine learning models using training features.

Preparation and governance: Good practice involves data cleansing, normalization, alignment of units, and clear documentation of assumptions. Reproducibility is aided by versioning inputs, recording data sources, and maintaining provenance. Validation and verification activities assess whether inputs are appropriate and whether the model responds plausibly to input changes.

Common challenges include missing or noisy data, bias in inputs, unit mismatches, data drift, and incompatibilities between input formats and model interfaces. Managing uncertainty and performing sensitivity analyses are standard techniques to understand how input variations affect outputs.