ISYE 6501 Midterm Quiz 1 with all the Correct Answers(Graded A+)

ISYE 6501 Midterm Quiz 1 with all the Correct Answers Course Midter… Step 2: … Step 2: … Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first 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 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. You have used 6 of 10 attempts. Reset Show Answer 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. Question 1 9/13 points (graded) Keyboard Help 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. You have used 1 of 1 attempts. Reset Show Answer FEEDBACK Correctly placed 8 items. Misplaced 1 item. 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 Question 2 2.19/3.0 points (graded) Select all of the following models that are designed for use with time series data: k-nearest-neighbor Principal component analysis ARIMA k-means CUSUM Logistic regression GARCH Exponential smoothing Random forest Linear regression Support vector machine You have used 1 of 1 attempt Answers are displayed within the problem Information for Questions 3a, 3b, 3c 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. Figure A Figure B Question 3a 2.4/3.0 points (graded) 3a. Select all of the following statements that are true. Figure A's classifier is based only on the value of x2 . Figure A has fewer classification errors in the training data. Figure A's classifier has a wider margin in the training data. Figure A's classifier incorrectly classifies exactly 4 white points in the training data. Figure A shows that the black point (7.2,1.4) is an outlier. You have used 1 of 1 attempt Answers are displayed within the problem Question 3b 2.25/3.0 points (graded) 3b. Select all of the following statements that are true. Figure B's classifier has a narrower margin in the training data. Figure B's classifier is more likely to be over-fit. Figure B's classifier incorrectly classifies exactly 5 white points in the training data. Figure B shows that the black point (7.2,1.4) should be white. You have used 1 of 1 attempt Answers are displayed within the problem Question 3c 1.5/3.0 points (graded) 3c. Select all of the following statements that are true. A new point at (3,3) would be classified as white by Figure A's classifier. A new point at (3,3) would be classified as white by Figure B's classifier. A new point at (3,3) would be classified as white by a k-nearest-neighbor algorithm for 5 ≤ k ≤ 10. In Figure A, if the training data had 1000 more white points to the right of the classifier, a 1000-nearest-neighbor algorithm would classify a new point at (3,3) as white. You have used 1 of 1 attempt Answers are displayed within the problem Question 3d 3.0/3.0 points (graded) In the soft classification SVM model where we select coefficients a0 ... am to minimize n m m ∑ max{0, 1 − (∑ ai xij + a0 ) yj} + C ∑ a2 j−1 i=1 i=1 3d. Select each of the following cases when we would want to decrease the value of C. We want a larger margin even if it induces more classification errors in the training set. We are willing to accept a smaller margin in order to reduce classification errors in the training set. Neither. You have used 1 of 1 attempt