ISYE 6501 EXAM PREP TEST BANK
VERIFIED 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
VERIFIED 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
