ISYE 6501 UPDATED ACTUAL QUESTIONS
AND CORRECT ANSWERS
◉ 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
Cons: Choosing K manually can bias it towards initial values
sensitive to outliers
◉ Grubbs Outlier Test
Answer: A formula that uses an outlier's value, the mean of the
data, and the standard deviation to determine whether or not the
data point is within the confidence interval for a normal
distribution or should be thrown out
◉ CUSUM
Answer: Change detection model that keeps a running total of the
amount that observations vary above the expected value. The
running total exceeds a preset threshold value, it indicates there
has been a change.
◉ CUSUM Pros/Cons
Answer: Pros: Best way to detect the small shifts of process mean
especially 0.5 to 2 SD from the target mean
AND CORRECT ANSWERS
◉ 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
Cons: Choosing K manually can bias it towards initial values
sensitive to outliers
◉ Grubbs Outlier Test
Answer: A formula that uses an outlier's value, the mean of the
data, and the standard deviation to determine whether or not the
data point is within the confidence interval for a normal
distribution or should be thrown out
◉ CUSUM
Answer: Change detection model that keeps a running total of the
amount that observations vary above the expected value. The
running total exceeds a preset threshold value, it indicates there
has been a change.
◉ CUSUM Pros/Cons
Answer: Pros: Best way to detect the small shifts of process mean
especially 0.5 to 2 SD from the target mean
