Statistics > QUESTIONS & ANSWERS > ISYE 6501 Midterm 1 • Due Mar 15 at 2am • Points 100 • Graded A+, 2022 predictor. (All)

ISYE 6501 Midterm 1 • Due Mar 15 at 2am • Points 100 • Graded A+, 2022 predictor.

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ISYE 6501 Midterm 1 • Due Mar 15 at 2am • Points 100 • Questions 45 • Available until Mar 15 at 2am • Time Limit 95 Minutes Instructions This quiz was locked Mar 15 at 2am. This elem... ent is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 1 9 / 13 pts 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 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 Classification viii. k-nearest-neighbor Clustering ix. Linear regression Validation x. Logistic regression Classification and Response prediction xi. Principal component analysis Validation xii. Random forest Classification and Response prediction xiii. Support vector machine ClassificationAnswer 1: Classification Clustering Correct! Response prediction Validation Variance estimation None of the other choices Answer 2: Correct! Classification and Response prediction Clustering Validation Variance estimation None of the other choices Answer 3: Classification and Response predictionClustering Correct! Validation Variance estimation None of the other choices Answer 4: Classification and Response prediction Clustering Validation Variance estimation Correct! None of the other choices Answer 5: Classification Clustering Correct! Response prediction ValidationVariance estimation None of the other choices Answer 6: Classification Clustering Respnse prediction Validation Correct! Variance estimation None of the other choices Answer 7: You Answered Classification Correct Answer Clustering Response prediction Validation Variance estimationNone of the other choices Answer 8: Correct Answer Classification and Response prediction You Answered Clustering Validation Variance estimation None of the other choices Answer 9: Classification Clustering Correct! Response prediction Validation Variance estimation None of the other choices Answer 10: Correct! Classification and Response predictionClustering Validation Variance estimation None of the other choices Answer 11: Classification and Response prediction Clustering Validation Variance estimation Correct! None of the other choices Answer 12: Correct! Classification and Response prediction Clustering Validation Variance estimationNone of the other choices Answer 13: Correct! Classification Clustering Response prediction Validation Variance estimation None of the other choices his element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 2 3 / 3 pts 2. For each of the following models, specify whether it is designed for use with attribute/feature data or time-series data: a. Exponential smoothing [ Select ] ["Attribute/feature data", "Time series data"] b. ARIMA [ Select ] ["Attribute/feature data", "Time series data"] c. k-means [ Select ] ["Attribute/feature data", "Time series data"] d. Principal component analysis [ Select ] ["Attribute/feature data", "Time series data"] e. Linear regression [ Select ] ["Attribute/feature data", "Time series data"] f. k-nearest-neighbor Attribute/feature data g. Random forest [ Select ] ["Attribute/feature data", "Time series data"] h. CUSUM [ Select ] ["Attribute/feature data", "Time series data"] i. Logistic regression [ Select ] ["Attribute/feature data", "Time series data"] j. Support vector machine [ Select ] ["Attribute/feature data", "Time series data"] k. GARCH [ Select ] ["Attribute/feature data", "Time series data"] Answer 1:Attribute/feature data Correct! Time series data Answer 2: Attribute/feature data Correct! Time series data Answer 3: Correct! Attribute/feature data Time series data Answer 4: Correct! Attribute/feature data Time series data Answer 5: Correct! Attribute/feature data Time series data Answer 6: Correct! Attribute/feature data Time series dataAnswer 7: Correct! Attribute/feature data Time series data Answer 8: Attribute/feature data Correct! Time series data Answer 9: Correct! Attribute/feature data Time series data Answer 10: Correct! Attribute/feature data Time series data Answer 11: Attribute/feature data Correct! Time series data Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. INFORMATION FOR QUESTIONS 3-12Figures 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. Figure AFigure B Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. INSTRUCTIONS FOR QUESTIONS 3-11 For each statement in Questions 3-11, select the choice that makes the statement true. Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 3 0.6 / 0.6 pts Figure A's classifier IS NOT based on the values of both x1 and x2. Answer 1: IS Correct! IS NOTMove To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 4 0.6 / 0.6 pts Figure A's classifier WOULD probably perform worse on test data than on the training data. Answer 1: Correct! WOULD WOULD NOT Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 5 0.6 / 0.6 pts Figure A's classifier has a WIDER margin than Figure B's classifier in the training data. Answer 1: NARROWER Correct! WIDER Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 6 0 / 0.6 pts Figure A's classifier incorrectly classifies EXACTLY 4 black points as white in the training data. Answer 1: EXACTLY 4 Correct! MORE OR FEWER THAN 4Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 7 0.6 / 0.6 pts Figure A DOES NOT SHOW that the black point (7.2,1.4) is an outlier. Answer 1: Correct! DOES NOT SHOW SHOWS Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 8 0.75 / 0.75 pts Figure B's classifier has a NARROWER margin in the training data than Figure A's classifier. Answer 1: Correct! NARROWER WIDER Move To... This element is a more accessible alternative to drag & drop reordering. Press Enter or Space to move this question. Question 9 0 / 0.75 pts Figure B's classifier would probably perform BETTER on test data than on training data. AnISYE 6501 Midterm 1 • Due Mar 15 at 2am • Points 100 • Questions 45swer 1: ISYE 6501 Midterm 1 • Due Mar 15 at 2am • Points 100 • Questions 45 [Show More]

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