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ISYE 6501 Midterm 2 Exam Newest 2025 Complete
100 questions and Correct Detailed Answers (Verified
Answers) |Already Graded A+
What is the Bias Variance Trade off? - ANSWER-When you
have high bias and low variance it leads to underfitting, less fit
to real patterns and less fit to random patterns
When you have low bias and high variance it leads to
overfitting, more fit to real patterns and more fit to random
patterns
Underfit - ANSWER-The less fit the model is the fewer
variables we use and the smaller the coefficients get
every prediction by the model gets closer to constant term a0
regardless of the value of x
removing variables and shrinking coefficients creates bias in the
model - model misses/minimizes real patterns in the data
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underfitting real effects while eliminating variance from random
effects
overfit - ANSWER-the more fit the model is the more variables
we use and the bigger the coefficients get, the more predictions
will differ
There's more variance between predictions and less bias
additional variance also includes variance due to random
patterns
What are the greedy variable selection methods? - ANSWER-
forward selection
backward selection
stepwise regression
What are global optimization variable methods? - ANSWER-
Lasso
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Elastic Net
What are quick methods of variable selection? - ANSWER-
forward selection
backward selection
stepwise regression - most common
what are the pros and cons of quick variable selection methods?
- ANSWER-good for initial analysis
can give variables with more fit to random patterns than you'd
like and appear to have a better fit
often doesn't perform as well when tested on other data
what are the slower methods of variable selection? - ANSWER-
Lasso
Elastic net
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what are the pros and cons of slower variable selection methods?
- ANSWER-slower to compute
give better predictive models
recommended to do unless just doing data exploration - then do
greedy methods first and use these methods to build more
refined model
what are the advantages of elastic net? - ANSWER-variable
selection benefits of Lasso
predictive benefits of ridge
what are the disadvantages of elastic net? - ANSWER-arbitrarily
rules out some correlated variables like lasso
underestimates coefficients of very predictive variables like
ridge regression
ISYE 6501 Midterm 2 Exam Newest 2025 Complete
100 questions and Correct Detailed Answers (Verified
Answers) |Already Graded A+
What is the Bias Variance Trade off? - ANSWER-When you
have high bias and low variance it leads to underfitting, less fit
to real patterns and less fit to random patterns
When you have low bias and high variance it leads to
overfitting, more fit to real patterns and more fit to random
patterns
Underfit - ANSWER-The less fit the model is the fewer
variables we use and the smaller the coefficients get
every prediction by the model gets closer to constant term a0
regardless of the value of x
removing variables and shrinking coefficients creates bias in the
model - model misses/minimizes real patterns in the data
,2|Page
underfitting real effects while eliminating variance from random
effects
overfit - ANSWER-the more fit the model is the more variables
we use and the bigger the coefficients get, the more predictions
will differ
There's more variance between predictions and less bias
additional variance also includes variance due to random
patterns
What are the greedy variable selection methods? - ANSWER-
forward selection
backward selection
stepwise regression
What are global optimization variable methods? - ANSWER-
Lasso
,3|Page
Elastic Net
What are quick methods of variable selection? - ANSWER-
forward selection
backward selection
stepwise regression - most common
what are the pros and cons of quick variable selection methods?
- ANSWER-good for initial analysis
can give variables with more fit to random patterns than you'd
like and appear to have a better fit
often doesn't perform as well when tested on other data
what are the slower methods of variable selection? - ANSWER-
Lasso
Elastic net
, 4|Page
what are the pros and cons of slower variable selection methods?
- ANSWER-slower to compute
give better predictive models
recommended to do unless just doing data exploration - then do
greedy methods first and use these methods to build more
refined model
what are the advantages of elastic net? - ANSWER-variable
selection benefits of Lasso
predictive benefits of ridge
what are the disadvantages of elastic net? - ANSWER-arbitrarily
rules out some correlated variables like lasso
underestimates coefficients of very predictive variables like
ridge regression
