In the context of parametrized predictors, the function g defines the ______ of the predictor, while the parameters determine its specific characteristics and behavior.

In parametrized predictors, the function g sets the overall form or shape of the predictor, while the parameters fine‑tune its exact behavior. Think of g as the blueprint that tells you what kind of model you have—linear, polynomial, or neural network—while the parameters fill in the details like weights or coefficients. For example, if g is a polynomial function, the parameters decide the degree and the coefficients, so changing them moves the curve up, down, or sideways. Thus, g defines the predictor’s structure, and the parameters adjust its specific characteristics.