ISYE 6501 MIDTERM 1 UPDATED ACTUAL QUESTIONS
WITH CORRECT ANSWERS
Question 1.
Rows
ANSWER
Data points are values in data tables
Question 2.
Columns
ANSWER
The 'answer' for each data point (response/outcome)
Question 3.
Structured Data
ANSWER
Quantitative, Categorical, Binary, Unrelated, Time Series
Question 4.
Unstructured Data
ANSWER
Text
Question 5.
Support Vector Model
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 6.
What do you want to find with a SVM model?
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 7.
What should the sum of the green points in a SVM model be?
ANSWER
The sum of green points should be greater than or equal to 1
Question 8.
What should the sum of the red points in a SVM model be?
ANSWER
The sum of red points should be less than or equal to -1
Question 9.
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 10.
First principal component
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 11.
Second principal component
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 12.
What if it's not possible to separate green and red points in a SVM model?
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 13.
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.
Question 14.
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
WITH CORRECT ANSWERS
Question 1.
Rows
ANSWER
Data points are values in data tables
Question 2.
Columns
ANSWER
The 'answer' for each data point (response/outcome)
Question 3.
Structured Data
ANSWER
Quantitative, Categorical, Binary, Unrelated, Time Series
Question 4.
Unstructured Data
ANSWER
Text
Question 5.
Support Vector Model
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 6.
What do you want to find with a SVM model?
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 7.
What should the sum of the green points in a SVM model be?
ANSWER
The sum of green points should be greater than or equal to 1
Question 8.
What should the sum of the red points in a SVM model be?
ANSWER
The sum of red points should be less than or equal to -1
Question 9.
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 10.
First principal component
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 11.
Second principal component
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 12.
What if it's not possible to separate green and red points in a SVM model?
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 13.
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.
Question 14.
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
