modelparameters

Model parameters are the adjustable values within a statistical or machine learning model that are learned from data during training. They define the functional mapping from inputs to outputs and determine how the model processes information. Examples include the coefficients in linear regression, the weights and biases in neural networks, and the parameters of probability distributions in Bayesian models. The complete set of parameters encapsulates the model’s learned knowledge and is used to make predictions on new data.

Hyperparameters, by contrast, are settings chosen before training that govern the learning process or the model architecture, such as the learning rate, the number of layers, and the strength of regularization. Hyperparameters are not directly learned from the data by the optimization algorithm.

Training involves optimizing a loss or objective function with respect to the parameters, using algorithms such as gradient descent, stochastic gradient descent, or Bayesian inference. The number of parameters reflects model capacity: more parameters can model complex patterns but increase the risk of overfitting and computational cost. Techniques to manage parameters include regularization, early stopping, parameter sharing, pruning, quantization, and model distillation to improve generalization and efficiency.

In some modeling approaches, parameters may be fixed or drawn from distributions, and in certain nonparametric methods the effective parameter set can grow with data. While individual parameters are often not easily interpretable, their collective behavior informs model decisions and feature influence. Parameters are typically stored as numerical arrays and require attention to precision and memory usage.