Isye 6501 final - Study guides, Class notes & Summaries

Isye 6501 Final exam Questions and Answers 100% Pass 1-norm - Answer- 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(|

Isye 6501 Final exam Questions and Answers 100% Pass 1-norm - Answer- 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 Final Exam Questions and Answers 100% Pass Factor Based Models classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model 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 Classical variable selection approaches 1. Forward selection 2. Backwards elimination 3. Stepwise reg...

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Autoregressive integrated moving average (ARIMA) - Time series model that uses differences between observations when data is nonstationary. Also called Box-Jenkins. Categorical Data - Data that classifies observations without quantitative meaning (for example, colors of cars) or where quantitative amounts are categorized (for example, "0-10, 11-20, ..."). Balking - An entity arrives to the queue, sees the size of the line (or some other attribute), and decides to leave the system. 2-norm...

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ISYE 6501 Final Exam Study Guide Latest Updated Georgia Institute of Technology

ISYE 6501 Final Questions and Answers Already Passed Support Vector Machine A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier SVM Pros/Cons Pros: It works really well with a clear margin of separation It is effective in high dimensional spaces. It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subse...

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