ISYE 6501 - Quiz 2(2026 / 2027) Questions and Verified Answers

ISYE 6501 - Quiz 2


Elastic Net
Constrain combination of absolute value of coefficients and their squares.

Choose tau and upsilon.


Ridge Regression
Take out absolute value term from Elastic Net.

Doesn't do variable selection, but does lead to better predictive models.




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LASSO
Constraint added to standard regression equation.

Budget t applied the model.


Greedy Variable Selection
Forward Selection
Backward Selection
Step-wise Regression


Global Variable Selection
LASSO

, Elastic Net


LASSO; How to choose t?
Depends on:
1) # of variables you want
2) Quality of the model as you add more variables


Pros and Cons:
1) Forward Selection
2) Backward Selection
3) Step-wise Regression
Pros:
Good for quick initial analysis to identify variables of potential importance.

Cons:
Don't perform well on additional data.


Pros and Cons:
1) LASSO
2) Elastic Net
Pros:
Better at prediction than greedy selection

Cons:
Slower


What's the difference between LASSO, Ridge Regression, and Elastic Net?

What's the difference between the sum of squared coefficients?
Quadratic term/constraint in ridge regression tends to shrinks/regularizes the coefficients.

Shrinking adds bias, but reduces variance, resulting in better model.


Pros and Cons of Elastic Net (Only)
Pros:
1) Variable selection benefits of LASSO
2) Predictive benefits of Ridge Regression

Cons: