ISYE 6501 Final Exam Questions and answers, 100% ACCURATE. RATED A+

ISYE 6501 Final Exam Questions and answers, 100% ACCURATE. RATED A+ Factor Based Models 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 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 1. Forward selection 2. Backwards elimination 3. Stepwise regression greedy algorithms Backward elimination 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 variable selection; classical 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