Computer Science > EXAM > COM SCI 6501XMidterm Quiz 1 _ ISYE6501x Courseware (All)

COM SCI 6501XMidterm Quiz 1 _ ISYE6501x Courseware

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Question 0 -- Practice with Drag & Drop 0 points possible (ungraded)  Keyboard Help Some of the quiz questions are Drag-and-Drop. You'll need to drag one or more answers to a location. Some an... swers might not be used at all, and some answers will be used once. To get full credit you might need to drag more than one answer to some locations, just one answer to other locations, and some locations might not have any correct answers. Please do this quick practice question. The question will give you feedback to make sure you've done it correctly, but the real quiz questions will not. x=1,y=7 x=2,y=3 x=1,y=411/22/2020 GT  Correctly placed 3 items.  Good work! You have completed this drag and drop problem. Note that: (1) There are two places you could've put (x=2,y=3); either one would be correct. (2) One location (x+y=2) had nothing dragged to it. Another location had two answers dragged to it. (3) One choice (x=1,y=7) was not dragged anywhere, since it wasn't correct for anything. You have used 5 of 10 attempts.  Reset Submit  Show Answer Question 1 11/13 points (graded)  Keyboard Help Drag each of the 13 models/methods to one of the 5 categories of question it is commonly used for, unless no correct category is listed for it. For models/methods that have more than one correct category, choose any one correct category; for models/methods that have no correct category listed, do not drag them. x=1,y=6 CUSUM Principal component  Correctly placed 9 items.  Misplaced 1 item.  Did not place 1 required item.  Good work! You have completed this drag and drop problem. Submit You have used 1 of 1 attempts.  Reset  Show Answer Support vector machine k-means ARIMA CART Exponential smoothing k-nearest-neighbor Linear regression ogistic regression Random forest Cross validation Question 2 2.46/3.0 points (graded) Select all of the following models that are designed for use with attribute/feature data (i.e., not time-series data):  You have used 1 of 1 attempt  Final attempt was used, highest score is 11.0 ARIMA CUSUM Support vector machine Random forest k-nearest-neighbor GARCH k-means Logistic regression Exponential smoothing Principal component analysis Linear regression [Show More]

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