7/6/26, 2:51 PM ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH ACCURATE ANSWERS || comprehensive questions and verified answers | GET …
ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH
ACCURATE ANSWERS || comprehensive
questions and verified answers | GET IT RIGHT
|2026!
Save Add to calendar
Terms in this set (102)
Main reasons to limit number of Overfitting - when # of factors is close to or larger
factors in model than # of data points; Simplicity - simple models
are better; reduce the number of correlated
variables; certain variables might be hard to collect
data or expensive; some variables are missing data
or hard to use
Why are simple models better? Less data is required, less chance of insignificant
factors, easier to interpret
Examples of factors that are illegal to race, sex, religion, marital status, or any factors that
use are highly correlated with forbidden ones
https://quizlet.com/1193389308/isye-6501-midterm-2-exam-questions-with-accurate-answers-comprehensive-questions-and-verified-answers-get-it-ri… 1/16
,7/6/26, 2:51 PM ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH ACCURATE ANSWERS || comprehensive questions and verified answers | GET …
What is forward selection? Start with a model with no factors, at each step,
find each best new factor to add to model, and put
it in if good enough (parameter of your choice like
p value<=0.15) improvement; when there is no
factor that is good enough or if we add enough
factors, we stop; can remove any factors at the end
What is backward selection? Start with all factors, and at each step, we find
worst factor, and remove from model and we keep
going until no factor bad enough to remove or we
reach the number of factors we want
What is stepwise regression? it is a combination of forward selection and
backward elimination. We can either start with all
factors or no factors and at each step we remove
or add a factor. As we go through the procedure
after adding each new factor and at the end we
eliminate right away factors that no longer appear;
Allows model to adjust if factor we earlier thought
we needed no longer seems necessary thanks to
new factors added
What is a greedy algorithm? At each step, it does the one thing that looks best
without future options are considered
What are potential metrics to p value, AIC, BIC, R^2
determine whether to add or remove
factors in variable selection?
https://quizlet.com/1193389308/isye-6501-midterm-2-exam-questions-with-accurate-answers-comprehensive-questions-and-verified-answers-get-it-ri… 2/16
, 7/6/26, 2:51 PM ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH ACCURATE ANSWERS || comprehensive questions and verified answers | GET …
What are methods that are based on LASSO, Elastic Net, Ridge Regression (not variable
optimization models that make selection)
decisions globally looking at all
options at the same time when it
comes to variable selection?
LASSO Approach Adds constraint to standard regression equation:
we want to minimize the sum of squared errors, but
also the sum of coefficients cannot be too large;
add threshold t - budget to use on coefficients;
need to scale data - uses absolute value of
coefficients
Factors to consider when picking the Number of variables and quality of model
right value of t for LASSO approach?
Elastic Net Constrain combination of absolute value of
coefficients and their squares, need to scale data,
choose t and lambda values
Ridge Regression Constrain the regression equation by the
coefficient squares - doesn't do variable selection
https://quizlet.com/1193389308/isye-6501-midterm-2-exam-questions-with-accurate-answers-comprehensive-questions-and-verified-answers-get-it-ri… 3/16
ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH
ACCURATE ANSWERS || comprehensive
questions and verified answers | GET IT RIGHT
|2026!
Save Add to calendar
Terms in this set (102)
Main reasons to limit number of Overfitting - when # of factors is close to or larger
factors in model than # of data points; Simplicity - simple models
are better; reduce the number of correlated
variables; certain variables might be hard to collect
data or expensive; some variables are missing data
or hard to use
Why are simple models better? Less data is required, less chance of insignificant
factors, easier to interpret
Examples of factors that are illegal to race, sex, religion, marital status, or any factors that
use are highly correlated with forbidden ones
https://quizlet.com/1193389308/isye-6501-midterm-2-exam-questions-with-accurate-answers-comprehensive-questions-and-verified-answers-get-it-ri… 1/16
,7/6/26, 2:51 PM ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH ACCURATE ANSWERS || comprehensive questions and verified answers | GET …
What is forward selection? Start with a model with no factors, at each step,
find each best new factor to add to model, and put
it in if good enough (parameter of your choice like
p value<=0.15) improvement; when there is no
factor that is good enough or if we add enough
factors, we stop; can remove any factors at the end
What is backward selection? Start with all factors, and at each step, we find
worst factor, and remove from model and we keep
going until no factor bad enough to remove or we
reach the number of factors we want
What is stepwise regression? it is a combination of forward selection and
backward elimination. We can either start with all
factors or no factors and at each step we remove
or add a factor. As we go through the procedure
after adding each new factor and at the end we
eliminate right away factors that no longer appear;
Allows model to adjust if factor we earlier thought
we needed no longer seems necessary thanks to
new factors added
What is a greedy algorithm? At each step, it does the one thing that looks best
without future options are considered
What are potential metrics to p value, AIC, BIC, R^2
determine whether to add or remove
factors in variable selection?
https://quizlet.com/1193389308/isye-6501-midterm-2-exam-questions-with-accurate-answers-comprehensive-questions-and-verified-answers-get-it-ri… 2/16
, 7/6/26, 2:51 PM ISYE 6501 Midterm 2 EXAM, || QUESTIONS WITH ACCURATE ANSWERS || comprehensive questions and verified answers | GET …
What are methods that are based on LASSO, Elastic Net, Ridge Regression (not variable
optimization models that make selection)
decisions globally looking at all
options at the same time when it
comes to variable selection?
LASSO Approach Adds constraint to standard regression equation:
we want to minimize the sum of squared errors, but
also the sum of coefficients cannot be too large;
add threshold t - budget to use on coefficients;
need to scale data - uses absolute value of
coefficients
Factors to consider when picking the Number of variables and quality of model
right value of t for LASSO approach?
Elastic Net Constrain combination of absolute value of
coefficients and their squares, need to scale data,
choose t and lambda values
Ridge Regression Constrain the regression equation by the
coefficient squares - doesn't do variable selection
https://quizlet.com/1193389308/isye-6501-midterm-2-exam-questions-with-accurate-answers-comprehensive-questions-and-verified-answers-get-it-ri… 3/16
