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ISYE 6501 - Midterm 2 Questions with 100% correct answers | verified | latest update 2024
ISYE 6501 - Midterm 2 Questions with 100% correct answers | verified | latest update 2024
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ISYE 6501 - Midterm 2when might overfitting occur - 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 - ANS-less data is required; less
chance of insignificant factors and easier to interpret
what is forward selection - 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 - 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 - 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? - ANS-Greedy algorithms - at each step
they take one thing that looks best
what is LASSO - 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 - 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 - ANS-if we don't the measure of the data
will artificially affect how big the coefficients need to be
What is elastic net? - 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 - 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? - 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) - 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 - 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? - 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
What are some downsides of surveys? - ANS-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: - ANS-Controlling
what is a blocking factor - ANS-a source of variability that is not of primary interest to
the experimenter
what is an example of a blocking factor - ANS-The type of car, sports car or family car,
is a blocking factor that it could account for some of the difference between red cars and
blue cars. Because sports cars are more likely to be red; if we account for the
difference, we can reduce the variability in our estimates
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