ISYE 6501X EXAM SCRIPT VERIFIED
QUESTIONS WITH ACCURATE 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
QUESTIONS WITH ACCURATE 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
