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ISYE6501x Midterm Quiz 1 - Audit Learners

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Question 1 13 points possible (graded)  Keyboard Help Drag each model to a type of question it is commonly used for. For models that have more than one correct answer, choose any one correct answer; for models that have ...

Question 1 13 points possible (graded)  Keyboard Help Drag each model to a type of question it is commonly used for. For models that have more than one correct answer, choose any one correct answer; for models that have no correct answer listed, do not drag them. ARIMA CART Cross validation CUSUM Exponential smoothing GARCH k-means k-nearest-neighbor Linear regression Logistic regression Principal component analysis 5/28/2019 Midterm Quiz 1 - Audit Learners | Midterm Quiz 1 - Audit Learners | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2019/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7244922e3d3a401994e816be1741097c/1?activate_block_id=block-v1%3AGTx%2BISYE6501x%2… 3/31 Submit You have used 0 of 1 attempts. Random forest Support vector machine 5/28/2019 Midterm Quiz 1 - Audit Learners | Midterm Quiz 1 - Audit Learners | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2019/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7244922e3d3a401994e816be1741097c/1?activate_block_id=block-v1%3AGTx%2BISYE6501x%2… 4/31 Question 2 0.0/3.0 points (graded) Select all of the following models that are designed for use with time series data: You have used 0 of 1 attempt FEEDBACK  Drag the items onto the image above.  Reset  Show Answer ARIMA  CUSUM  Exponential Smoothing  GARCH  k-nearest-neighbor Submit 5/28/2019 Midterm Quiz 1 - Audit Learners | Midterm Quiz 1 - Audit Learners | ISYE6501x Courseware | edX https://courses.edx.org/courses/course-v1:GTx+ISYE6501x+1T2019/courseware/a8e7783f3b6d4b21bbf5720bb6f02a92/7244922e3d3a401994e816be1741097c/1?activate_block_id=block-v1%3AGTx%2BISYE6501x%2… 5/31 Answers are displayed within the problem INFORMATION FOR QUESTIONS 3A, 3C FIGURES A AND B SHOW THE TRAINING DATA FOR A CLASSIFICATION PROBLEM, USING TWO PREDICTORS (X AND X ) TO SEPARATE BETWEEN BLACK AND WHITE POINTS. THE DASHED LINES ARE THE CLASSIFIERS. Figure A Figure B Question 3a 0.0/3.0 points (graded) Figure B shows an SV

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