ISYE 6501 - test 2 WITH RESOLUTIONS.
,when might overfitting occur - correct ans: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 ans:less data is
required; less chance of insignificant factors and easier to interpret
what is forward selection - correct ans: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 ans: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 ans: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 ans:Greedy
algorithms - at each step they take one thing that looks best
what is LASSO - correct ans: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 ans: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 ans:if we don't the
measure of the data will artificially affect how big the coefficients need to be
What is elastic net? - correct ans: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 ans: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 ans: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 ans: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 ans: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 ans: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
,when might overfitting occur - correct ans: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 ans:less data is
required; less chance of insignificant factors and easier to interpret
what is forward selection - correct ans: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 ans: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 ans: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 ans:Greedy
algorithms - at each step they take one thing that looks best
what is LASSO - correct ans: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 ans: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 ans:if we don't the
measure of the data will artificially affect how big the coefficients need to be
What is elastic net? - correct ans: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 ans: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 ans: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 ans: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 ans: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 ans: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
