ISYE 6501 - Midterm 2 UPDATED ACTUAL Questions and CORRECT Answers
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Course
ISYE 6501
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ISYE 6501
ISYE 6501 - Midterm 2 UPDATED
ACTUAL Questions and CORRECT
Answers
when might overfitting occur - CORRECT ANSWER- 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 comp...
ISYE 6501 - Midterm 2 UPDATED
ACTUAL Questions and CORRECT
Answers
when might overfitting occur - CORRECT ANSWER✔✔- 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 ANSWER✔✔- less data is
required; less chance of insignificant factors and easier to interpret
what is forward selection - CORRECT ANSWER✔✔- 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 ANSWER✔✔- 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 ANSWER✔✔- 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 ANSWER✔✔- Greedy
algorithms - at each step they take one thing that looks best
what is LASSO - CORRECT ANSWER✔✔- 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 ANSWER✔✔- 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 ANSWER✔✔- if we don't, the
measure of the data will artificially affect how big the coefficients need to be
What is elastic net? - CORRECT ANSWER✔✔- 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
ANSWER✔✔- 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 ANSWER✔✔- The
coefficients values are squared so they go closer to zero or regularizes them, but the
coefficient values are never equal to zero
What are the pros and cons of Greedy Algorithms (Forward selection, stepwise elimination,
stepwise regression) - CORRECT ANSWER✔✔- 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, Ridge and Elastic Net - CORRECT ANSWER✔✔-
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 ANSWER✔✔- Ridge Regression and LASSO.
Advantages: variable selection from LASSO and Predictive benefits of Ridge.
Disadvantages: Arbitrarily rules out some correlated variables (e.g. LASSO doesn't know
which one should be left out); Underestimates coefficients of very predictive variables (i.e.
Ridge Regression)
What are some downsides of surveys? - CORRECT ANSWER✔✔- Even if you have 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
ANSWER✔✔- Controlling
what is a blocking factor *** - CORRECT ANSWER✔✔- a source of variability that is not
of primary interest to the experimenter
what is an example of a blocking factor - CORRECT ANSWER✔✔- 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
Under what conditions should you run A/B tests - CORRECT ANSWER✔✔- When you can
collect data quickly. When the data is representative and the amount of data is small
compared to the whole population
Do you have to decide the sample size ahead of time for A/B tests - CORRECT
ANSWER✔✔- no, and we can run the hypothesis test anytime we want
What is full factorial design - CORRECT ANSWER✔✔- you test every combination and
then use ANOVA to determine importance of each factor
What is fractional factorial design - CORRECT ANSWER✔✔- when you test a subset of the
entire set of combinations
What is a balanced design? - CORRECT ANSWER✔✔- You test each choice the same # of
times and each pair of choices the same # of times
When is regression effective in variable selection? - CORRECT ANSWER✔✔- If there aren't
significant interactions between the factors.
What is exploration? - CORRECT ANSWER✔✔- focusing on getting more information; in
this case, to determine with more certainty which ad is really the best. Uses a decided upon
formula to randomly or otherwise select a path
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