ISYE 6501 MIDTERM 1 COMPREHENSIVE TEST
PAPER WITH COMPLETE SOLUTIONS
◉ 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.
◉ Clustering.
Answer: takes a set of data points, dividing them into groups so each
group contains points that are close to eachother or similar.
◉ Distance Norms.
Answer: Given 2 points x and y with coordinates x1, x2 and y1, y2 --
the distance between them is the square root of x1-y1 squared + x2-
y2 squared.
◉ rectilinear distance norms.
Answer: Absolute value of distance norms
◉ P-norm distance.
Answer: generalized version of both distance equations where p
would be 2 for a straight-line distance and P would be 1 for a
rectilinear distance
3rd most common value for P is infinity
PAPER WITH COMPLETE SOLUTIONS
◉ 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.
◉ Clustering.
Answer: takes a set of data points, dividing them into groups so each
group contains points that are close to eachother or similar.
◉ Distance Norms.
Answer: Given 2 points x and y with coordinates x1, x2 and y1, y2 --
the distance between them is the square root of x1-y1 squared + x2-
y2 squared.
◉ rectilinear distance norms.
Answer: Absolute value of distance norms
◉ P-norm distance.
Answer: generalized version of both distance equations where p
would be 2 for a straight-line distance and P would be 1 for a
rectilinear distance
3rd most common value for P is infinity
