Question

What is a key difference between stepwise regression and lasso regression?

Answer 1

Stepwise regression and lasso regression differ in terms of how variables are selected. Stepwise regression iteratively adds or removes variables based on criteria, while lasso regression uses a regularization technique to automatically select variables by shrinking their coefficients to zero.

Step-by-step explanation:

A key difference between stepwise regression and lasso regression is the approach used to select variables. Stepwise regression is an iterative process that starts with an empty model and adds or removes variables at each step based on certain criteria, such as p-values or AIC values. On the other hand, lasso regression uses a regularization technique that automatically performs variable selection by shrinking the coefficients of less important variables to zero.

Stepwise regression:

1. Start with an empty model.
2. Add variables one by one based on certain criteria.
3. Remove variables that no longer meet the criteria.
4. Repeat until no more improvements can be made.

Lasso regression:

1. Include all variables in the model.
2. Regularize the coefficients using a penalty term.
3. Some coefficients are shrunk to zero, effectively removing them from the model.