ISYE 6501 FINAL EXPECTED
QUESTIONS WITH VERIFIED ANSWERS
Question 1.
Support Vector Machine
ANSWER
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
Question 2.
SVM Pros/Cons
ANSWER
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.
Question 3.
K-nearest neighbor (K-NN)
ANSWER
An unsupervised classification algorithm. Looks at the X number of closest points to the new
one and classifies as whichever is most common.
, Question 4.
K-nearest neighbor (K-NN) Pros/Cons
ANSWER
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
Question 5.
k-fold cross validation
ANSWER
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
Question 6.
K Fold cross Validation Pros/Cons
ANSWER
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
Question 7.
k-means clustering
ANSWER
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.
QUESTIONS WITH VERIFIED ANSWERS
Question 1.
Support Vector Machine
ANSWER
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
Question 2.
SVM Pros/Cons
ANSWER
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.
Question 3.
K-nearest neighbor (K-NN)
ANSWER
An unsupervised classification algorithm. Looks at the X number of closest points to the new
one and classifies as whichever is most common.
, Question 4.
K-nearest neighbor (K-NN) Pros/Cons
ANSWER
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
Question 5.
k-fold cross validation
ANSWER
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
Question 6.
K Fold cross Validation Pros/Cons
ANSWER
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
Question 7.
k-means clustering
ANSWER
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.
