Information Technology > QUESTIONS & ANSWERS > Georgia Institute Of Technology - ISYE 6501/ISYE6501 Midterm Quiz 1 with Correct Answers (All)

Georgia Institute Of Technology - ISYE 6501/ISYE6501 Midterm Quiz 1 with Correct Answers

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Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack rst before taking the Midterm Quiz 1) 95 Minute Time Limit Instructions Work alone. Do not collaborate with or copy from anyone else. You... may use any of the following resources: One sheet (both sides) of handwritten (not photocopied or scanned) notes If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like Calvin): Good Luck!    Question 0 -- Practice with Drag & Drop 0 points possible (ungraded)  Keyboard Help 11/11/2019 Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first before taking the Midterm Quiz 1) | Step 2: Midterm Quiz 1 - GT St… https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+2T2019b/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7d4f7b9ba5e84153afb… 2/24 Some of the quiz questions are Drag-and-Drop. You'll need to drag one or more answers to a location. Some answers 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. FEEDBACK  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 6 of 10 attempts.  Reset Submit  Show Answer Question 1 9/13 points (graded)  Keyboard Help x=1, y=7 x=2, y=3 x=1, y=4 x=1, y=6 11/11/2019 Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first before taking the Midterm Quiz 1) | Step 2: Midterm Quiz 1 - GT St… https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+2T2019b/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7d4f7b9ba5e84153afb… 3/24 Drag each model or method to a category of question it is commonly used for. 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. FEEDBACK  Correctly placed 8 items.  Misplaced 1 item. Submit You have used 1 of 1 attempts.  Reset  Show Answer CUSUM Principal component analysis Support vector machine k-means ARIMA CART Exponential smoothing k-nearest-neighbor Linear regression Logistic regression Random forest Cross validation GARCH 11/11/2019 Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first before taking the Midterm Quiz 1) | Step 2: Midterm Quiz 1 - GT St… https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+2T2019b/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7d4f7b9ba5e84153afb… 4/24 Question 2 2.19/3.0 points (graded) Select all of the following models that are designed for use with time series data:  You have used 1 of 1 attempt  Did not place 3 required items.  Good work! You have completed this drag and drop problem.  Final attempt was used, highest score is 9.0 k-nearest-neighbor Principal component analysis ARIMA  k-means CUSUM  Logistic regression GARCH  Exponential smoothing  Random forest Linear regression Support vector machine Submit 11/11/2019 Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first before taking the Midterm Quiz 1) | Step 2: Midterm Quiz 1 - GT St… https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+2T2019b/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7d4f7b9ba5e84153afb… 5/24 Answers are displayed within the problem Information for Questions 3a, 3b, 3c Figures A and B show the training data for a soft classication problem, using two predictors (x and x ) to separate between black and white points. The dashed lines are the classiers found using SVM. Figure A uses a linear kernel, and Figure B uses a nonlinear kernel that required tting 16 parameter values. [Show More]

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