Which of the following is NOT a type of regularization technique used in linear regression to prevent overfitting?

To answer the question about regularization techniques in linear regression, we first need to identify what regularization techniques are commonly used and then determine which option does not belong to that category.

Regularization techniques are methods used to limit overfitting by adding a penalty term to the loss function used to train the model. The two most common types of regularization in linear regression are:

1. Lasso Regression (L1 Regularization): This technique adds a penalty equal to the absolute value of the magnitude of coefficients, which can lead to some coefficients being exactly zero.

2. Ridge Regression (L2 Regularization): This technique adds a penalty equal to the square of the magnitude of coefficients, which tends to shrink the coefficients but does not set them to zero.

Another less common method includes:

3. Elastic Net: This combines L1 and L2 regularization to benefit from both techniques.

Given these definitions, techniques that do not involve modifying the complexity of the model or do not add penalties for large coefficients would not be considered regularization techniques.

Thus, options like "None of the above," or any method not related to penalizing coefficients would typically be the correct answers in multiple-choice settings.

It is essential to consider the specific options given in the question to identify which one is NOT a regularization technique.