ISYE 6501 FINAL CUMULATIVE REVIEW
TEST SOLUTION COMPLETE GUIDE GRADED
A+.
◍ 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.
◍ 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.
◍ 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
◍ 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
◍ 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
◍ 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.
◍ k-means pros and cons. Answer: Pros: Simple to implement
Scales well to large data sets
Easily adaptable
TEST SOLUTION COMPLETE GUIDE GRADED
A+.
◍ 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.
◍ 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.
◍ 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
◍ 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
◍ 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
◍ 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.
◍ k-means pros and cons. Answer: Pros: Simple to implement
Scales well to large data sets
Easily adaptable
