ISYE 6501-MIDTERM 2 EXAM |ACTUAL COMPLETE
EXAM QUESTIONS AND CORRECT ANSWERS
(VERIFIED ANSWERS ) ALREADY GRADED
A+.Georgia Institute of Technology-Main Campus
what is LASSO
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
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
if we don't the measure of the data will artificially affect how big the
coefficients need to be
,What is elastic net?
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
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?
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)
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
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?
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?
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:
Controlling
, what is a blocking factor
a source of variability that is not of primary interest to the experimenter
what is an example of a blocking factor
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
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
no, and we can run the hypothesis test anytime we want
What is full factorial design
you test every combination and then use ANOVA to determine importance
of each factor
What is fractional factorial design
when you test a subset of the entire set of combinations
EXAM QUESTIONS AND CORRECT ANSWERS
(VERIFIED ANSWERS ) ALREADY GRADED
A+.Georgia Institute of Technology-Main Campus
what is LASSO
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
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
if we don't the measure of the data will artificially affect how big the
coefficients need to be
,What is elastic net?
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
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?
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)
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
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?
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?
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:
Controlling
, what is a blocking factor
a source of variability that is not of primary interest to the experimenter
what is an example of a blocking factor
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
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
no, and we can run the hypothesis test anytime we want
What is full factorial design
you test every combination and then use ANOVA to determine importance
of each factor
What is fractional factorial design
when you test a subset of the entire set of combinations
