ISYE 6501 Final Exam Questions and

ISYE 6501 Final Exam Questions and Answers (Solved Papers) Factor Based Models - Correct Answers ✅classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model Why limit number of factors in a model? 2 reasons - Correct

Answers ✅overfitting: when # of factors is close to or larger

than # of data points. Model may fit too closely to random effects

simplicity: simple models are usually better

Classical variable selection approaches - Correct Answers ✅1. Forward selection

• Backwards elimination
• Stepwise regression

greedy algorithms Backward elimination - Correct Answers ✅variable selection; classical Opposite of forward selection. Start with model with all factors, at each step find worst factor and remove from model. Continue until no more to add, # of factor threshold is satisfied. Remove factors at the end that were not good enough Forward selection - Correct Answers ✅variable selection; classical 1 / 3

ISYE 6501 Final Exam Questions and Answers (Solved Papers) Start with model with no factors, at each step find best new factor to add. Continue until none bad enough to remove, # of factor threshold is satisfied. Remove factors at the end that were not good enough Stepwise regression - Correct Answers ✅variable selection; classical Combination of forward selection and backwards elimination.Start with all or no factors. Each step remove/add a factor. As it continues, after adding in new factor we eliminate right away any factors that may be good. Helps model adjust when new factors are added, goodness values change Ways of determining if factors are good enough in variable selection - Correct Answers ✅p-value, Rsquared, AIC, BIC Greedy algorithm - Correct Answers ✅At each step, it does the one thing that looks best without taking future options into consideration. Good for initial analysis

• Forward selection
• Backwards elimination
• Stepwise regression

Global variable selection approaches - Correct Answers

✅1. LASSO 2 / 3

ISYE 6501 Final Exam Questions and Answers (Solved Papers)

• Elastic Net

Slower, but tend to give better predictive models LASSO - Correct Answers ✅variable selection; global

• SCALE the date (as with any constrained sum of

coefficients)

• add a constraint to the standard regression equation
• minimize sum of squared errors
• T = limit or "budget" on how large the sum of squared errors

can get. Budget will be used on most important coefficients

• Method for limiting the number of variables in a model by

limiting the sum of all coefficients' absolute values. Can be very helpful when number of data points is less than number of factors.Elastic Net - Correct Answers ✅variable selection; global

• SCALE the date (as with any constrained sum of

coefficients)

• T = limit or "budget" on how large the sum of squared errors

can get. Budget will be used on most important coefficients

• Combination of lasso and ridge regression.
• Variable selection benefits of LASSO
• Predictive benefits of ridge regression
• / 3