2024 newest|ISYE 6414 Final Exam 6. The selected variables using best subset
Review| UPDATE|COMPREHENSIVE regression are the best ones in explaining and
predicting the response variables. - --ANSWER---
QUESTIONS AND VERIFIED
False
SOLUTIONS/CORRECT
ANSWERS|GET 100% ACCURATE!!
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
1. If there are variables that need to be used to 10. Backward and forward stepwise regression
control the bias selection in the model, they will generally provide different sets of selected
should forced to be in the model and not being variables when p, the number of predicting
part of the variable selection process. - -- variables, is large. - --ANSWER---True
ANSWER---True
11. All regularized regression approaches can be
2. Penalization in linear regression models used for variable selection. - --ANSWER---False
means penalizing for complex models, that is,
models with a large number of predictors. - --
ANSWER---True 12. Before performing regularized regression,
we need to standardize or rescale the pre-
dicting variables. - --ANSWER---True
3. Elastic net regression uses both penalties of
the ridge and lasso regression and hence
combines the benefits of both. - --ANSWER--- 13. The larger the number of predicting
True variables is, the larger the bias but the smaller
the variance is. - --ANSWER---False
4. Variable selection can be applied to
regression problems when the number of pre- 14. Variable selection is a simple and solved
dicting variables is larger than the number of statistical problem since we can implement it
observations. - --ANSWER---True using the R statistical software. - --ANSWER---
False
5. The lasso regression performs well under
multicollineariy. - --ANSWER---False
Review| UPDATE|COMPREHENSIVE regression are the best ones in explaining and
predicting the response variables. - --ANSWER---
QUESTIONS AND VERIFIED
False
SOLUTIONS/CORRECT
ANSWERS|GET 100% ACCURATE!!
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
1. If there are variables that need to be used to 10. Backward and forward stepwise regression
control the bias selection in the model, they will generally provide different sets of selected
should forced to be in the model and not being variables when p, the number of predicting
part of the variable selection process. - -- variables, is large. - --ANSWER---True
ANSWER---True
11. All regularized regression approaches can be
2. Penalization in linear regression models used for variable selection. - --ANSWER---False
means penalizing for complex models, that is,
models with a large number of predictors. - --
ANSWER---True 12. Before performing regularized regression,
we need to standardize or rescale the pre-
dicting variables. - --ANSWER---True
3. Elastic net regression uses both penalties of
the ridge and lasso regression and hence
combines the benefits of both. - --ANSWER--- 13. The larger the number of predicting
True variables is, the larger the bias but the smaller
the variance is. - --ANSWER---False
4. Variable selection can be applied to
regression problems when the number of pre- 14. Variable selection is a simple and solved
dicting variables is larger than the number of statistical problem since we can implement it
observations. - --ANSWER---True using the R statistical software. - --ANSWER---
False
5. The lasso regression performs well under
multicollineariy. - --ANSWER---False
