ISYE 6501 Final Test Questions with Verified
Correct Answers
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 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)
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
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
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
Pros: Validates Performance of model
Can create balance across predicted features classes
Cons: Doesn't work well with time series data
The aggregate scores of your model could miss some important extreme values or overpower
them so theyre harder to pick up on
k-means clustering
Unsupervised learning heuristic that sets x starts by assigning x number of cluster centers,
then clusters all data points into each of them based on distance. The center point of each
cluster is then calculated and all data points are again re clustered. Repeat process until no-
data points change clusters. Ideal number of clusters can be identified via elbow diagram.
k-means pros and cons
Pros: Simple to implement
Scales well to large data sets
Easily adaptable
Cons: Choosing K manually can bias it towards initial values
sensitive to outliers
Correct Answers
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 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)
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
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
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
Pros: Validates Performance of model
Can create balance across predicted features classes
Cons: Doesn't work well with time series data
The aggregate scores of your model could miss some important extreme values or overpower
them so theyre harder to pick up on
k-means clustering
Unsupervised learning heuristic that sets x starts by assigning x number of cluster centers,
then clusters all data points into each of them based on distance. The center point of each
cluster is then calculated and all data points are again re clustered. Repeat process until no-
data points change clusters. Ideal number of clusters can be identified via elbow diagram.
k-means pros and cons
Pros: Simple to implement
Scales well to large data sets
Easily adaptable
Cons: Choosing K manually can bias it towards initial values
sensitive to outliers
