hpar

hpar is an acronym used in multiple fields, and there is no universally accepted single meaning. In electronics and electrical engineering contexts, h-parameters (sometimes referred to in informal shorthand as hpar) describe a linear two-port network's small-signal behavior with respect to input current and voltage. The four parameters, h11, h12, h21, and h22, relate input and output variables for a given operating point and are used to analyze and design amplifiers, filters, and other circuits.

In data science and machine learning contexts, hpar is often shorthand for hyperparameters. Hyperparameters are configuration settings that are not learned from the data during training but govern the learning process and model structure. Examples include learning rate, batch size, number of layers, and regularization strength. The choice of hyperparameters can significantly affect training dynamics, convergence, and model performance, and they are typically tuned using methods such as grid search, random search, or Bayesian optimization.

Beyond electronics and ML, hpar may appear as an internal code, project name, or acronym with field-specific meaning in specialized domains. Because the term is not standardized, precise definitions usually depend on the context and accompanying explanation.

If a particular field or source uses hpar with a distinct meaning, providing that context will yield a more precise definition. This article can be expanded with domain-specific interpretations as they become established.