ISYE 6501 Midterm 1 (FA23)

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




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,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




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,10/15/24, 9:05 ISYE 6501 Midterm 1
AM (FA23)




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, 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.




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