ISYE 6501 FINAL COMPREHENSIVE EXAM
QUESTIONS AND SOLUTIONS
◉ Soft Classifier.
Answer: Account for errors in SVM classification. Trading off
minimizing errors we make and maximizing the margin.
To trade off between them, we pick a lambda value and minimize a
combination of error and margin. As lambda gets large, this term
gets large.
The importance of a large margin outweighs avoiding mistakes and
classifying known data points.
◉ Should you scale your data in a SVM model?.
Answer: Yes, so the orders of magnitude are approximately the
same.
Data must be in bounded range.
Common scaling: data between 0 and 1
a. Scale factor by factor
b. Linearly
◉ How should you find which coefficients to hold value in a SVM
model?.
,Answer: If there is a coefficient who's value is very close to 0, means
the corresponding attribute is probably not relevant for
classification.
◉ Does SVM work the same for multiple dimensions?.
Answer: Yes
◉ Does a SVM classifier need to be a straight line?.
Answer: No, SVM can be generalized using kernel methods that
allow for nonlinear classifiers. Software has a kernel SVM function
that you can use to solve for both linear and nonlinear classifiers.
◉ Can classification questions be answered as probabilities in SVM?.
Answer: Yes.
◉ K Nearest Neighbor Algorithm.
Answer: Find the class of the new point, Pick the k closest points to
the new one, the new points class is the most common amongst the
k neighbors.
◉ What should you do about varying level of importance across
attributes with K Nearest Neighbors?.
, Answer: Some attributes might be more important than others to
the classification --- can deal with this by weighting each
dimension's distance differently.
Unimportant attributes may be removed as they are not very
important for the classification.
◉ What is the difference between real and random effects in
validation?.
Answer: Real effects: same in all data sets
Random effects: different in all data sets
◉ How should one generally split their data set?.
Answer: Training (building models) / Validation (picking model) /
Test (estimate performance)
◉ Rotating versus randomness when validating data?.
Answer: Rotation: can make sure each part of the data is equally
separated
Randomness: no chance of bias
◉ K-fold Cross-Validation.
Answer: takes number of sections (k) and tests against eachother so
you don't have to worry about what is being left out. Gives a better
estimate of model quality.
QUESTIONS AND SOLUTIONS
◉ Soft Classifier.
Answer: Account for errors in SVM classification. Trading off
minimizing errors we make and maximizing the margin.
To trade off between them, we pick a lambda value and minimize a
combination of error and margin. As lambda gets large, this term
gets large.
The importance of a large margin outweighs avoiding mistakes and
classifying known data points.
◉ Should you scale your data in a SVM model?.
Answer: Yes, so the orders of magnitude are approximately the
same.
Data must be in bounded range.
Common scaling: data between 0 and 1
a. Scale factor by factor
b. Linearly
◉ How should you find which coefficients to hold value in a SVM
model?.
,Answer: If there is a coefficient who's value is very close to 0, means
the corresponding attribute is probably not relevant for
classification.
◉ Does SVM work the same for multiple dimensions?.
Answer: Yes
◉ Does a SVM classifier need to be a straight line?.
Answer: No, SVM can be generalized using kernel methods that
allow for nonlinear classifiers. Software has a kernel SVM function
that you can use to solve for both linear and nonlinear classifiers.
◉ Can classification questions be answered as probabilities in SVM?.
Answer: Yes.
◉ K Nearest Neighbor Algorithm.
Answer: Find the class of the new point, Pick the k closest points to
the new one, the new points class is the most common amongst the
k neighbors.
◉ What should you do about varying level of importance across
attributes with K Nearest Neighbors?.
, Answer: Some attributes might be more important than others to
the classification --- can deal with this by weighting each
dimension's distance differently.
Unimportant attributes may be removed as they are not very
important for the classification.
◉ What is the difference between real and random effects in
validation?.
Answer: Real effects: same in all data sets
Random effects: different in all data sets
◉ How should one generally split their data set?.
Answer: Training (building models) / Validation (picking model) /
Test (estimate performance)
◉ Rotating versus randomness when validating data?.
Answer: Rotation: can make sure each part of the data is equally
separated
Randomness: no chance of bias
◉ K-fold Cross-Validation.
Answer: takes number of sections (k) and tests against eachother so
you don't have to worry about what is being left out. Gives a better
estimate of model quality.
