ISYE 6501 Midterm Questions and Answers
Question : Rows
Correct answer: Data points are values in data tables
Question : Columns
Correct answer: The 'answer' for each data point
(response/outcome)
Question : Structured Data
Correct answer: Quantitative, Categorical, Binary, Unrelated,
Time Series
Question : Unstructured Data
Correct answer: Text
Question : Support Vector Model
Correct answer: Supervised machine learning algorithm used
for both classification and regression challenges.
Mostly used in classification problems by plotting each data item as a point in n-dimensional space (n is the number of features you have) with the value of each feature being the value of a particular coordinate.Then you classify by finding a hyperplane that differentiates the 2 classes very well. Support vectors are simply the coordinates of individual observation -- it best segregates the two classes (hyperplane / line).
Question : What do you want to find with a SVM model?
Correct answer: Find values of a0, a1,...,up to am that
classifies the points correctly and has the maximum gap or margin between the parallel lines.
Question : What should the sum of the green points in a SVM
model be?
Correct answer: The sum of green points should be greater
than or equal to 1
Question : What should the sum of the red points in a SVM
model be?
Correct answer: The sum of red points should be less than or
equal to -1
Question : What should the total sum of green and red points
be?
Correct answer: The total sum of all green and red points
should be equal to or greater than 1 because yj is 1 for green and -1 for red.
Question : First principal component
Correct answer: PCA -- a linear combination of original
predictor variables which captures the maximum variance in the data set. It determines the direction of highest variability in the data. Larger the variability captured in first component, larger the information captured by component. No other component can have variability higher than first principal component.it minimizes the sum of squared distance between a data point and the line.
Question : Second principal component
Correct answer: PCA -- also a linear combination of original
predictors which captures the remaining variance in the data set and is uncorrelated with Z¹. In other words, the correlation between first and second component should is zero.
Question : What if it's not possible to separate green and red
points in a SVM model?
Correct answer: Utilize a soft classifier -- In a soft
classification context, we might add an extra multiplier for each type of error with a larger penalty, the less we want to accept mis-classifying that type of point.
Question : Soft Classifier
Correct 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.