Isye 6501 Study guides, Class notes & Summaries

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ISYE 6501 Midterm 1 EXAM  QUESTIONS WITH 100%  SOLUTIONS LATEST UPDATE  2023/2024 Popular
  • ISYE 6501 Midterm 1 EXAM QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024

  • Exam (elaborations) • 6 pages • 2023
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  • ISYE 6501 Midterm 1 EXAM QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024 True or false: In a regression tree, every leaf of the tree has a different regression model that might use different attributes, have different coefficients, etc. - ANSWER True - Each leaf's individual model is tailored to the subset of data points that follow all of the branches leading to the leaf. True or false: Tree-based approaches can be used for other models besides regression. - ANSWER True ...
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ISYE 6501 - Midterm 1 Exam 2023| Correct Questions & Answers| Verified 100 Popular
  • ISYE 6501 - Midterm 1 Exam 2023| Correct Questions & Answers| Verified 100

  • Exam (elaborations) • 19 pages • 2023 Popular
  • What do descriptive questions ask? - ANSWERSWhat happened? (e.g., which customers are most alike) What do predictive questions ask? - ANSWERSWhat will happen? (e.g., what will Google's stock price be?) What do prescriptive questions ask? - ANSWERSWhat action(s) would be best? (e.g., where to put traffic lights) What is a model? - ANSWERSReal-life situation expressed as math. What do classifiers help you do? - ANSWERSdifferentiate What is a soft classifier and when is it used? - AN...
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ISYE 6501 Final Exam Actual Questions and Answers 2023-2024 with complete solution
  • ISYE 6501 Final Exam Actual Questions and Answers 2023-2024 with complete solution

  • Exam (elaborations) • 60 pages • 2023
  • ISYE 6501 Final Exam Actual Questions and Answers with complete solution
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ISYE 6501 - Midterm 2 Questions And Answers
  • ISYE 6501 - Midterm 2 Questions And Answers

  • Exam (elaborations) • 13 pages • 2023
  • when might overfitting occur - Answer- when the # of factors is close to or larger than the # of data points causing the model to potentially fit too closely to random effects Why are simple models better than complex ones - Answer- less data is required; less chance of insignificant factors and easier to interpret what is forward selection - Answer- we select the best new factor and see if it's good enough (R^2, AIC, or p-value) add it to our model and fit the model with the current set ...
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ISYE 6501 - Midterm 1 Exam – Questions And Answers
  • ISYE 6501 - Midterm 1 Exam – Questions And Answers

  • Exam (elaborations) • 23 pages • 2023
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  • ISYE 6501 - Midterm 1 Exam – Questions And Answers
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ISyE 6501 Final Quiz
  • ISyE 6501 Final Quiz

  • Exam (elaborations) • 49 pages • 2023
  • ISyE 6501 Final Quiz
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Isye 6501 Final exam with 100% correct answers
  • Isye 6501 Final exam with 100% correct answers

  • Exam (elaborations) • 99 pages • 2023
  • 1-norm Similar to rectilinear distance; measures the straight-line length of a vector from the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is square root(|
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ISYE 6501 Midterm Quiz 2 with all the Correct Answers(Graded A+)
  • ISYE 6501 Midterm Quiz 2 with all the Correct Answers(Graded A+)

  • Exam (elaborations) • 27 pages • 2022
  • ISYE 6501 Midterm Quiz 2 with all the Correct Answers(Graded A+) ISYE 6501 Midterm Quiz 2 with all the Correct Answers(Graded A+) ISYE 6501 Midterm Quiz 2 with all the Correct Answers(Graded A+) ISYE 6501 Midterm Quiz 2 with all the Correct Answers(Graded A+)
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ISYE 6501  FINAL EXAM WITH COMPLETE  SOLUTION 2022/2023
  • ISYE 6501 FINAL EXAM WITH COMPLETE SOLUTION 2022/2023

  • Exam (elaborations) • 15 pages • 2022
  • ISYE 6501 FINAL EXAM WITH COMPLETE SOLUTION 2022/2023 1. Factor Based Models: classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model 2. Why limit number of factors in a model? 2 reasons: overfitting: when # of factors is close to or larger than # of data points. Model may fit too closely to random effects simplicity: simple models are usually better 3. Classical variable selection approaches: 1. Forward selection 2. Backwards eli...
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