ISYE 6501 - Midterm 2
Questions and Answers
Denning [Date] [Course title]
,when might overfitting occur - Correct Answers:s :when the # of factors is close to or larger than the # of
data points causing the model to potentially fit too closely to random effects
Why are simple models better than complex ones - Correct Answers:s :less data is required; less chance
of insignificant factors and easier to interpret
what is forward selection - Correct Answers:s :we select the best new factor and see if it's good enough
(R^2, AIC, or p-value) add it to our model and fit the model with the current set of factors. Then at the
end we remove factors that are lower than a certain threshold
what is backward elimination - Correct Answers:s :we start with all factors and find the worst on a
supplied threshold (p = 0.15). If it is worse we remove it and start the process over. We do that until we
have the number of factors that we want and then we move the factors lower than a second threshold
(p = .05) and fit the model with all set of factors
what is stepwise regression - Correct Answers:s :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.
what type of algorithms are stepwise selection? - Correct Answers:s :Greedy algorithms - at each step
they take one thing that looks best
what is LASSO - Correct Answers:s :a variable selection method where the coefficients are determined
by both minimizing the squared error and the sum of their absolute value not being over a certain
threshold t
How do you choose t in LASSO - Correct Answers:s :use the lasso approach with different values of t and
see which gives the best trade off
why do we have to scale the data for LASSO - Correct Answers:s :if we don't the measure of the data will
artificially affect how big the coefficients need to be
, What is elastic net? - Correct Answers:s :A variable selection method that works by minimizing the
squared error and constraining the combination of absolute values of coefficients and their squares
what is a key difference between stepwise regresson and lasso regression - Correct Answers:s :If the
data is not scaled, the coefficients can have artificially different orders of magnitude, which means
they'll have unbalanced effects on the lasso constraint.
Why doesn't Ridge Regression perform variable selection? - Correct Answers:s :The coefficients values
are squared so they go closer to zero or regularizes them
What are the pros and cons of Greedy Algorithms (Forward selection, stepwise elimination, stepwise
regression) - Correct Answers:s :Good for initial analysis but often don't perform as well on other data
because they fit more to random effects than you'd like and appear to have a better fit
What are the pros and cons of LASSO and elastic net - Correct Answers:s :They are slower but help make
models that make better predictions
Which two methods does elastic net look like it combines and what are the downsides from it? - Correct
Answers:s :Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive benefits of LASSO.
Disadvantages: Arbitrarily rules out some correlated variables like LASSO (don't know which one that is
left out should be); Underestimates coefficients of very predictive variables like Ridge Regresison
What are some downsides of surveys? - Correct Answers:s :Even if you what appears to be a
representative sample in simple ways, maybe it isn't in more complex ways.
If we're testing to see whether red cars sell for higher prices than blue cars, we need to account for the
type and age of the cars in our data set. This is called: - Correct Answers:s :Controlling
what is a blocking factor - Correct Answers:s :a source of variability that is not of primary interest to the
experimenter
Questions and Answers
Denning [Date] [Course title]
,when might overfitting occur - Correct Answers:s :when the # of factors is close to or larger than the # of
data points causing the model to potentially fit too closely to random effects
Why are simple models better than complex ones - Correct Answers:s :less data is required; less chance
of insignificant factors and easier to interpret
what is forward selection - Correct Answers:s :we select the best new factor and see if it's good enough
(R^2, AIC, or p-value) add it to our model and fit the model with the current set of factors. Then at the
end we remove factors that are lower than a certain threshold
what is backward elimination - Correct Answers:s :we start with all factors and find the worst on a
supplied threshold (p = 0.15). If it is worse we remove it and start the process over. We do that until we
have the number of factors that we want and then we move the factors lower than a second threshold
(p = .05) and fit the model with all set of factors
what is stepwise regression - Correct Answers:s :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.
what type of algorithms are stepwise selection? - Correct Answers:s :Greedy algorithms - at each step
they take one thing that looks best
what is LASSO - Correct Answers:s :a variable selection method where the coefficients are determined
by both minimizing the squared error and the sum of their absolute value not being over a certain
threshold t
How do you choose t in LASSO - Correct Answers:s :use the lasso approach with different values of t and
see which gives the best trade off
why do we have to scale the data for LASSO - Correct Answers:s :if we don't the measure of the data will
artificially affect how big the coefficients need to be
, What is elastic net? - Correct Answers:s :A variable selection method that works by minimizing the
squared error and constraining the combination of absolute values of coefficients and their squares
what is a key difference between stepwise regresson and lasso regression - Correct Answers:s :If the
data is not scaled, the coefficients can have artificially different orders of magnitude, which means
they'll have unbalanced effects on the lasso constraint.
Why doesn't Ridge Regression perform variable selection? - Correct Answers:s :The coefficients values
are squared so they go closer to zero or regularizes them
What are the pros and cons of Greedy Algorithms (Forward selection, stepwise elimination, stepwise
regression) - Correct Answers:s :Good for initial analysis but often don't perform as well on other data
because they fit more to random effects than you'd like and appear to have a better fit
What are the pros and cons of LASSO and elastic net - Correct Answers:s :They are slower but help make
models that make better predictions
Which two methods does elastic net look like it combines and what are the downsides from it? - Correct
Answers:s :Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive benefits of LASSO.
Disadvantages: Arbitrarily rules out some correlated variables like LASSO (don't know which one that is
left out should be); Underestimates coefficients of very predictive variables like Ridge Regresison
What are some downsides of surveys? - Correct Answers:s :Even if you what appears to be a
representative sample in simple ways, maybe it isn't in more complex ways.
If we're testing to see whether red cars sell for higher prices than blue cars, we need to account for the
type and age of the cars in our data set. This is called: - Correct Answers:s :Controlling
what is a blocking factor - Correct Answers:s :a source of variability that is not of primary interest to the
experimenter
