ISYE 6501 Final Quiz GT Students exam solution Georgia Institute Of Technology

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Que 12 / 13 pts stion 1 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 c... ategory, the one it is most Answer 1: Answer 2: commonly used for; for models/methods that have no correct category listed, select"None". i. ARIMA Response prediction ii. CART Classification and Response prediction iii. Cross validation Validation iv. CUSUM None of the other choices v. Exponential smoothing Response prediction vi. GARCH Variance estimation vii. kmeans Clustering viii. k-nearest-neighbor Classification and Response prediction ix. Linear regression Response prediction x. Logistic regression Classification and Response prediction xi. Principal component analysis Classification and Response prediction xii. Random forest Classification and Response prediction xiii. Support vector machine Classification CCoorrrreecctt!! Response prediction CCoorrrreecctt!! Classification and Response prediction Answer 3: Answer 4: Answer 5: Answer 6: Answer 7: Answer 8: Answer 9: Answer 10: Answer 11: CCoorrrreecctt!! Validation CCoorrrreecctt!! None of the other choices CCoorrrreecctt!! Response prediction CCoorrrreecctt!! Variance estimation CCoorrrreecctt!! Clustering CCoorrrreecctt!! Classification and Response prediction CCoorrrreecctt!! Response prediction CCoorrrreecctt!! Classification and Response prediction Answer 12: Answer 13: YYoouu AAnnsswweerreedd Classification and Response prediction CCoorrrreecctt AAnnsswweerr None of the other choices CCoorrrreecctt!! Classification and Response prediction CCoorrrreecctt!! Classification Q 3 / 3 pts uestion 2 2. For each of the following models, specify whether it is designed for use with attribute/feature data or time-series data: a. k-nearest-neighbor b. Support vector machine c. Random forest d. GARCH Time series data e. Logistic regression f. Principal component analysis g. Exponential smoothing h. Linear regression i. CUSUM Answer 1: Answer 2: Answer 3: Answer 4: Answer 5: Answer 6: Answer 7: Answer 8: j. ARIMA k. k-means CCoorrrreecctt!! Attribute/feature data CCoorrrreecctt!! Attribute/feature data CCoorrrreecctt!! Attribute/feature data CCoorrrreecctt!! Time series data CCoorrrreecctt!! Attribute/feature data CCoorrrreecctt!! Attribute/feature data CCoorrrreecctt!! Time series data Answer 9: Answer 10: Answer 11: CCoorrrreecctt!! Attribute/feature data CCoorrrreecctt!! Time series data CCoorrrreecctt!! Time series data CCoorrrreecctt!! Attribute/feature data INFORMATION FOR QUESTIONS 3-12 Figures A and B show the training data for a soft classification problem, using two predictors (x and x ) 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. 1 2 Figure A Figure B INSTRUCTIONS FOR QUESTIONS 3-11 For each statement in Questions 3-11, select the choice that makes the statement true. Que 0.6 / 0.6 pts stion 3 Answer 1: Figure A's classifier IS based only on the value of x1. CCoorrrreecctt!! IS Q 0.6 / 0.6 pts uestion 4 Figure A has MORE classification errors in the training data than Figure B. Answer 1: CCoorrrreecctt!! MORE Q 0.6 / 0.6 pts uestion 5 Answer 1: Figure A's classifier has a WIDER margin than Figure B's classifier in the training data. CCoorrrreecctt!! WIDER [Show More]

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