4/15/26, 6:12 PM ISYE 6501 -Exam 2 Wks 8 - 12 Flashcards | Quizlet
ISYE 6501 -Exam 2 Wks 8 - 12
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Final Exam ISYE 6501 - Midterm 2 GATECH Midterm 2 Analytics ISYE 65
49 terms 160 terms 51 terms 414 term
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Terms in this set (88)
Building simpler models with fewer factors helps avoid A. Overfitting
which problems? D. Difficulty of interpretation
A. Overfitting
B. Low prediction quality
C. Bias in the most important factors
D. Difficulty in interpretation
Two main reasons to limit # of factors in a model. 1. Overfitting
2. Simplicity
When is overfitting likely to happen? When the number of factors is close to the number of data points.
How does using a # of factors that is close to the number The model too closely fits the random efffects. It fits that data set well, but fails to
of data points cause overfitting? predict well on a new data set.
Three reasons simple models are better than complex 1. Less data is required
models. 2. Less chance of including insignificant factors
3. Easier to interpret
Which of these is a key difference between stepwise A. If the data isn't scaled, the coefficients can have artificially different orders of
regression and lasso regression? magnitude, which means they'll have unbalanced effects on the lasso constraint.
A. Lasso regression requires the data to first be scaled
B. Stepwise regression gives many models to choose
from, while lasso gives just one.
Name three greedy approaches to variable selection. 1. Forward Selection
2. Backward Elimination
3. Stepwise Regression
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, 4/15/26, 6:12 PM ISYE 6501 -Exam 2 Wks 8 - 12 Flashcards | Quizlet
Name two global approaches to variable selection. 1. Lasso
2. Elastic Net
What does a greedy approach to variable selection At each step, the model does one thing that looks best without taking future
mean? options into consideration. A more classical approach.
How does forward selection differ from backward Forward selection starts with zero factors and backward elimination starts with all
elimination? factors.
What is stepwise regression? A combination of forward selection and backward elimination. At each step, a
variable is considered for addition or elimination based on some prespecified
criterion.
Name two approaches to use with stepwise regression. 1. Start with all factors
2. Start with no factors
What restriction does the lasso approach add? The sum of the coefficients can't be too large. "t"
How does lasso use the t value? It uses that on the most important coefficients and the others will be zero so
those factors won't be part of the model.
What do you need to with the data whenever you're Scale the data.
constraining the sum of coefficients?
The value of t in the lasso approach depends on what 1. The number of variables you want.
two things? 2. The quality of the model as you allow more variables
What is the best approach to find the best value of t with Try lasso with different values of t and pick the value that has the best tradeoff
lasso? between number of variables and quality of the model.
What constraint does Elastic Net add? Elastic Net constrains the absolute value of the coefficients and their squares.
Name two things similar about Lasso and Elastic Net. 1. You have to scale the data for both.
2. You have to pick the best value of t for both.
Do you have to scale the data for Elastic Net? Yes
What change can you make to elastic net to get a model Remove the absolute value term.
called ridge regression?
Does ridge regression do variable selection? No.
What is the advantage of ridge regression? It can lead to better predictive models.
When two predictors are highly correlated, which of the B. Ridge regression will choose smaller (in an absolute sense) non-zero
following statements is true? coefficients for both models. By nature, it may underestimate the effect of the
A. Lasso regression will usually have non-zero factors.
coefficients for both predictors.
B. Ridge regression will usually have non-zero
coefficients for both predictors.
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ISYE 6501 -Exam 2 Wks 8 - 12
3.5 (2 reviews)
Save
Students also studied
Flashcard sets Study guides
Final Exam ISYE 6501 - Midterm 2 GATECH Midterm 2 Analytics ISYE 65
49 terms 160 terms 51 terms 414 term
trinh_monica Preview oanise Preview micahlowens9 Preview oan
Terms in this set (88)
Building simpler models with fewer factors helps avoid A. Overfitting
which problems? D. Difficulty of interpretation
A. Overfitting
B. Low prediction quality
C. Bias in the most important factors
D. Difficulty in interpretation
Two main reasons to limit # of factors in a model. 1. Overfitting
2. Simplicity
When is overfitting likely to happen? When the number of factors is close to the number of data points.
How does using a # of factors that is close to the number The model too closely fits the random efffects. It fits that data set well, but fails to
of data points cause overfitting? predict well on a new data set.
Three reasons simple models are better than complex 1. Less data is required
models. 2. Less chance of including insignificant factors
3. Easier to interpret
Which of these is a key difference between stepwise A. If the data isn't scaled, the coefficients can have artificially different orders of
regression and lasso regression? magnitude, which means they'll have unbalanced effects on the lasso constraint.
A. Lasso regression requires the data to first be scaled
B. Stepwise regression gives many models to choose
from, while lasso gives just one.
Name three greedy approaches to variable selection. 1. Forward Selection
2. Backward Elimination
3. Stepwise Regression
https://quizlet.com/274657316/isye-6501-exam-2-wks-8-12-flash-cards/ 1/7
, 4/15/26, 6:12 PM ISYE 6501 -Exam 2 Wks 8 - 12 Flashcards | Quizlet
Name two global approaches to variable selection. 1. Lasso
2. Elastic Net
What does a greedy approach to variable selection At each step, the model does one thing that looks best without taking future
mean? options into consideration. A more classical approach.
How does forward selection differ from backward Forward selection starts with zero factors and backward elimination starts with all
elimination? factors.
What is stepwise regression? A combination of forward selection and backward elimination. At each step, a
variable is considered for addition or elimination based on some prespecified
criterion.
Name two approaches to use with stepwise regression. 1. Start with all factors
2. Start with no factors
What restriction does the lasso approach add? The sum of the coefficients can't be too large. "t"
How does lasso use the t value? It uses that on the most important coefficients and the others will be zero so
those factors won't be part of the model.
What do you need to with the data whenever you're Scale the data.
constraining the sum of coefficients?
The value of t in the lasso approach depends on what 1. The number of variables you want.
two things? 2. The quality of the model as you allow more variables
What is the best approach to find the best value of t with Try lasso with different values of t and pick the value that has the best tradeoff
lasso? between number of variables and quality of the model.
What constraint does Elastic Net add? Elastic Net constrains the absolute value of the coefficients and their squares.
Name two things similar about Lasso and Elastic Net. 1. You have to scale the data for both.
2. You have to pick the best value of t for both.
Do you have to scale the data for Elastic Net? Yes
What change can you make to elastic net to get a model Remove the absolute value term.
called ridge regression?
Does ridge regression do variable selection? No.
What is the advantage of ridge regression? It can lead to better predictive models.
When two predictors are highly correlated, which of the B. Ridge regression will choose smaller (in an absolute sense) non-zero
following statements is true? coefficients for both models. By nature, it may underestimate the effect of the
A. Lasso regression will usually have non-zero factors.
coefficients for both predictors.
B. Ridge regression will usually have non-zero
coefficients for both predictors.
https://quizlet.com/274657316/isye-6501-exam-2-wks-8-12-flash-cards/ 2/7
