ISYE6414 FINAL PRACTICE TEST BANK EXAMPREP FULL SOLUTION SET HIGHLY ACCURATE REVIEW

ISYE6414 FINAL PRACTICE TEST BANK
EXAMPREP FULL SOLUTION SET HIGHLY
ACCURATE REVIEW

●● 2. Penalization in linear regression models means penalizing for
complex models, that is, models with a large number of predictors..
Answer: True


●● 3. Elastic net regression uses both penalties of the ridge and lasso
regression and hence combines the benefits of both..
Answer: True


●● 4. Variable selection can be applied to regression problems when the
number of pre- dicting variables is larger than the number of
observations..
Answer: True


●● 5. The lasso regression performs well under multicollineariy..
Answer: False


●● 6. The selected variables using best subset regression are the best
ones in explaining and predicting the response variables..
Answer: False

, ●● 8. The lasso regression requires a numerical algorithm to minimize
the penalized sum of least squares..
Answer: True


●● 9. An unbiased estimator of the prediction risk is the training risk..
Answer: False


●● 10. Backward and forward stepwise regression will generally
provide different sets of selected variables when p, the number of
predicting variables, is large..
Answer: True


●● 11. All regularized regression approaches can be used for variable
selection..
Answer: False


●● 12. Before performing regularized regression, we need to standardize
or rescale the pre- dicting variables..
Answer: True


●● 13. The larger the number of predicting variables is, the larger the
bias but the smaller the variance is..
Answer: False