10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
1. For each of the 13 models/methods, select the choice that
includes the category of question it is commonly used for. For
models/methods that have more than one correct category, the
one it is most commonly used for; for models/methods that have
no correct category listed, select "None".
i. ARIMA [ Select ]
ii. CART [ Select ]
iii. Cross validation Validation
iv. CUSUM [ Select ]
v. Exponential smoothing [ Select ]
vi. GARCH [ Select ]
kmeans [ Select ]
vii.
viii. k-nearest-neighbor [ Select ]
ix. Linear regression [ Select ]
x. Logistic regression [ Select ]
Principal component analysis [ Select ]
xi.
xii. Random forest [ Select ]
xiii. Support vector machine [ Select ]
Answer 1:
Correct!
Response prediction
Answer 2:
Correct!
Classification and Response prediction
about:bl 1/
,10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
Answer 3:
Correct!
Validation
Answer 4:
Correct!
None of the other choices
Answer 5:
Correct! Response prediction
Answer 6:
Correct!
Variance estimation
Answer 7:
Correct!
Clustering
Answer 8:
Correct!
Classification and Response prediction
Answer 9:
Correct!
Response prediction
Answer 10:
Correct! Classification and Response prediction
Answer 11:
Correct! None of the other choices
Answer 12:
Correct! Classification and Response prediction
Answer 13:
Correct!
Classification
about:bl 2/
,10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
about:bl 3/
, 10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
Answer 5:
Correct!
Attribute/feature data
Answer 6:
Correct!
Attribute/feature data
Answer 7:
Correct! Attribute/feature data
Answer 8:
Correct!
Attribute/feature data
Answer 9:
Correct!
Time series data
Answer 10:
Correct!
Attribute/feature data
Answer 11:
Correct!
Time series data
INFORMATION FOR QUESTIONS 3-12
Figures A and B show the training data for a soft classification
problem, using two predictors (x1 and x2) to separate between
black and white points. The dashed lines are the classifiers found
using SVM. Figure A uses a linear kernel, and Figure B uses a
nonlinear kernel that required fitting 16 parameter values.
about:bl 4/
AM (FA23)
1. For each of the 13 models/methods, select the choice that
includes the category of question it is commonly used for. For
models/methods that have more than one correct category, the
one it is most commonly used for; for models/methods that have
no correct category listed, select "None".
i. ARIMA [ Select ]
ii. CART [ Select ]
iii. Cross validation Validation
iv. CUSUM [ Select ]
v. Exponential smoothing [ Select ]
vi. GARCH [ Select ]
kmeans [ Select ]
vii.
viii. k-nearest-neighbor [ Select ]
ix. Linear regression [ Select ]
x. Logistic regression [ Select ]
Principal component analysis [ Select ]
xi.
xii. Random forest [ Select ]
xiii. Support vector machine [ Select ]
Answer 1:
Correct!
Response prediction
Answer 2:
Correct!
Classification and Response prediction
about:bl 1/
,10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
Answer 3:
Correct!
Validation
Answer 4:
Correct!
None of the other choices
Answer 5:
Correct! Response prediction
Answer 6:
Correct!
Variance estimation
Answer 7:
Correct!
Clustering
Answer 8:
Correct!
Classification and Response prediction
Answer 9:
Correct!
Response prediction
Answer 10:
Correct! Classification and Response prediction
Answer 11:
Correct! None of the other choices
Answer 12:
Correct! Classification and Response prediction
Answer 13:
Correct!
Classification
about:bl 2/
,10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
about:bl 3/
, 10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)
Answer 5:
Correct!
Attribute/feature data
Answer 6:
Correct!
Attribute/feature data
Answer 7:
Correct! Attribute/feature data
Answer 8:
Correct!
Attribute/feature data
Answer 9:
Correct!
Time series data
Answer 10:
Correct!
Attribute/feature data
Answer 11:
Correct!
Time series data
INFORMATION FOR QUESTIONS 3-12
Figures A and B show the training data for a soft classification
problem, using two predictors (x1 and x2) to separate between
black and white points. The dashed lines are the classifiers found
using SVM. Figure A uses a linear kernel, and Figure B uses a
nonlinear kernel that required fitting 16 parameter values.
about:bl 4/
