Shrinkage refers to
A) lowered accuracy in a prediction equation when it is applied to a new sample.
B) combining many variables in order to predict the most amount of variance.
C) effects of unreliable instruments underestimating the correlation.
D) a measure of the error found in a regression equation.

Question

Quizplus · Accepted Answer

Shrinkage refers to the phenomenon where the coefficients of a predictive model are "shrunk" towards a central value, usually zero. This is done in order to avoid overfitting of the model to the training data, which can lead to reduced accuracy when the model is applied to new data. Therefore, option A is the correct choice.

Shrinkage Refers to