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
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
