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Exam (elaborations)

ISYE 6501 Final Questions with Correct Answers.

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  • ISYE 6501x
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  • ISYE 6501x

ISYE 6501 Final Questions with Correct Answers.

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  • October 14, 2024
  • 10
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • ISYE 6501x
  • ISYE 6501x
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NETEXPERT
ISYE 6501 Final Questions
and Answers


Denning [Date] [Course title]

, Support Vector Machine - Correct Answers:s :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 - Correct Answers:s :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 subset of training points in the decision function (called support vectors), so it is also memory
efficient.

Cons: Not good for very large data sets

Not good for when the data set has more noise i.e. target classes are overlapping

Doesn't directly provide probability estimates.



K-nearest neighbor (K-NN) - Correct Answers:s :An unsupervised classification algorithm. Looks at the X
number of closest points to the new one and classifies as whichever is most common.



K-nearest neighbor (K-NN) Pros/Cons - Correct Answers:s :Pros: No assumptions about data

Easy to understand/Interpret

Varsatile



Cons: Computationally expensive because algorithm stores all training data

Sensitive to irrelevant features and scale of data



k-fold cross validation - Correct Answers:s :Validation Technique where data is divided into X number of
data subsets. Each subset is then used as a for testing while the rest are used for training. The algorithm
then rotates through each subset and averages the results



K Fold cross Validation Pros/Cons - Correct Answers:s :Pros: Validates Performance of model

Can create balance across predicted features classes

Cons: Doesn't work well with time series data

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