ISYE 6501 QUIZ 2 UPDATED ACTUAL
QUESTIONS WITH CORRECT ANSWERS
Question 1.
Elastic Net
ANSWER
Constrain combination of absolute value of coefficients and their squares.
Choose tau and upsilon.
Question 2.
Ridge Regression
ANSWER
Take out absolute value term from Elastic Net.
Doesn't do variable selection, but does lead to better predictive models.
Question 3.
LASSO
ANSWER
Constraint added to standard regression equation.
Budget t applied the model.
,Question 4.
Greedy Variable Selection
ANSWER
Forward Selection
Backward Selection
Step-wise Regression
Question 5.
Global Variable Selection
ANSWER
LASSO
Elastic Net
Question 6.
LASSO; How to choose t?
ANSWER
Depends on:
1) # of variables you want
2) Quality of the model as you add more variables
Question 7.
Pros and Cons:
1) Forward Selection
2) Backward Selection
3) Step-wise Regression
ANSWER
Pros:
Good for quick initial analysis to identify variables of potential importance.
Cons:
Don't perform well on additional data.
,Question 8.
Pros and Cons:
1) LASSO
2) Elastic Net
ANSWER
Pros:
Better at prediction than greedy selection
Cons:
Slower
Question 9.
What's the difference between LASSO, Ridge Regression, and Elastic Net?
What's the difference between the sum of squared coefficients?
ANSWER
Quadratic term/constraint in ridge regression tends to shrinks/regularizes the coefficients.
Shrinking adds bias, but reduces variance, resulting in better model.
Question 10.
Pros and Cons of Elastic Net (Only)
ANSWER
Pros:
1) Variable selection benefits of LASSO
2) Predictive benefits of Ridge Regression
Cons:
1) Arbitrarily rules out some correlated variables like LASSO.
2) Underestimates coefficients of very predictive variables like Ridge Regression.
, Question 11.
Design of Experiments (DOE)
ANSWER
Choosing a set of tests to be made to find the effect of input variables on an outcome.
Question 12.
Blocking Factor
ANSWER
Something that creates variation in an experiment which is not the subject of study.
Question 13.
A/B Testing Requirements
ANSWER
1) Collect data quickly
2) Data must be representative
3) Amount of data is small compared to the whole population
Question 14.
Bernouilli Distribution
ANSWER
Question 15.
Full Factorial Design
ANSWER
Test of all possible combinations of factor values over multiple factors to find each one's
effect, and interaction effects, on the outcome.
Question 16.
Binomial Distribution
ANSWER
The probability distribution of a binomial random variable.
QUESTIONS WITH CORRECT ANSWERS
Question 1.
Elastic Net
ANSWER
Constrain combination of absolute value of coefficients and their squares.
Choose tau and upsilon.
Question 2.
Ridge Regression
ANSWER
Take out absolute value term from Elastic Net.
Doesn't do variable selection, but does lead to better predictive models.
Question 3.
LASSO
ANSWER
Constraint added to standard regression equation.
Budget t applied the model.
,Question 4.
Greedy Variable Selection
ANSWER
Forward Selection
Backward Selection
Step-wise Regression
Question 5.
Global Variable Selection
ANSWER
LASSO
Elastic Net
Question 6.
LASSO; How to choose t?
ANSWER
Depends on:
1) # of variables you want
2) Quality of the model as you add more variables
Question 7.
Pros and Cons:
1) Forward Selection
2) Backward Selection
3) Step-wise Regression
ANSWER
Pros:
Good for quick initial analysis to identify variables of potential importance.
Cons:
Don't perform well on additional data.
,Question 8.
Pros and Cons:
1) LASSO
2) Elastic Net
ANSWER
Pros:
Better at prediction than greedy selection
Cons:
Slower
Question 9.
What's the difference between LASSO, Ridge Regression, and Elastic Net?
What's the difference between the sum of squared coefficients?
ANSWER
Quadratic term/constraint in ridge regression tends to shrinks/regularizes the coefficients.
Shrinking adds bias, but reduces variance, resulting in better model.
Question 10.
Pros and Cons of Elastic Net (Only)
ANSWER
Pros:
1) Variable selection benefits of LASSO
2) Predictive benefits of Ridge Regression
Cons:
1) Arbitrarily rules out some correlated variables like LASSO.
2) Underestimates coefficients of very predictive variables like Ridge Regression.
, Question 11.
Design of Experiments (DOE)
ANSWER
Choosing a set of tests to be made to find the effect of input variables on an outcome.
Question 12.
Blocking Factor
ANSWER
Something that creates variation in an experiment which is not the subject of study.
Question 13.
A/B Testing Requirements
ANSWER
1) Collect data quickly
2) Data must be representative
3) Amount of data is small compared to the whole population
Question 14.
Bernouilli Distribution
ANSWER
Question 15.
Full Factorial Design
ANSWER
Test of all possible combinations of factor values over multiple factors to find each one's
effect, and interaction effects, on the outcome.
Question 16.
Binomial Distribution
ANSWER
The probability distribution of a binomial random variable.
