smoothingparameter
A smoothing parameter is a tuning parameter used to control the degree of smoothness of an estimated function or signal. In smoothing methods, it balances fidelity to observed data against the roughness of the resulting fit. Larger values produce smoother estimates with lower variance but potentially higher bias; smaller values fit the data more closely and may capture noise as structure.
Common contexts include kernel smoothing, smoothing splines, and LOESS. In kernel methods such as kernel regression
LOESS or LOWESS uses a span parameter that controls the fraction of data used in local fits,
Selecting an appropriate smoothing parameter is a model-choice problem and is typically guided by cross-validation, generalized
It is important to distinguish the smoothing parameter from inherent data noise; it is a tuning parameter