pg. 1
ISYE 6501 - MIDTERM 2 EXAM LATEST 2025|
COMPLETE 250 ACTUAL EXAM QUESTIONS AND
VERIFIED ANSWERS (100% CORRECT ANSWERS)
ALREADY GRADED A+
what type of algorithms are stepwise selection? - ANSWER - Greedy algorithms - at each step they take one thing that looks best
what is LASSO - 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 - 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 - ANSWER - if we don't, the measure of the data will artificially affect how big the coefficients need to be
What is elastic net? - 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 *** - 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.
- / 4
pg. 2 Why doesn't Ridge Regression perform variable selection? - 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) - 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 - 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? - 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? - 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: - ANSWER - Controlling
what is a blocking factor *** - ANSWER - a source of variability that is not of primary interest to the experimenter 2 / 4
pg. 3 what is an example of a blocking factor - 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
What is exploration? - 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
what is exploitation - ANSWER - we're focused on getting immediate value using 'best known probability'; in this example, to show the add that seems to be doing best so far, because it seems to be most likely to be clicked.
what is the multi-armed bandit approach and how does it balance exploration and exploitation. - ANSWER - We start with no info and have an equal probability of selecting each alternative. After performing some tests, we've gotten more information, so we can update the probabilities of each one being best and start assigning new tests according to those probabilities. We keep testing multiple alternatives; so, we're still doing exploration. But we make it more likely to pick the best ones so we're also doing exploitation
What are some of the parameters in the multi-armed bandit approach - ANSWER - number of tests between recalculating probabilities; how to update the probabilities; and how to pick an alternative to test based on probabilities and/or expected values. For updating we can use bayesian updates or estimate from the observed distribution
What are common reasons that data sets are missing values? - ANSWER -
- a person accidentally types in the wrong value
- a person did not want to reveal the true value 3 / 4
pg. 4
- an automated system did not work correctly to record the value
What are some examples of why there might be bias in missing data -
ANSWER - * Income: people with higher incomes are more likely to omit
this answer
* Radar gun: a car that passes the radar gun very slowly might be treated
as an anomaly and its speed might be recorded in the system
- Heart transplants: If there's a variable "date of death" it will be missing for
patients still living and thus the missing data will naturally include more successful transplant cases
What are three ways of dealing with missing data that don't require imputation - ANSWER - discard the data, use categorical variables to indicate missing data, estimate missing values
What are the pros and cons of throwing away missing data - ANSWER -
Pros: not potentially introducing errors; easy to implement
Cons: don't want to lose to many data points; potential for censored or
biased missing data
What is the categorical variable approach - ANSWER - If the data is categorical, we just add another category "missing". With quantitative variables you include interactions variables between the categorical variable and other variables.
Why wouldn't you want to fill in missing quantitative variabes with 0 - ANSWER - It can lead to problems if some types of data points are more likely than others to have missing data. The coefficients of the other variables might be pulled in one direction or another to try to account for the missing data
- / 4