ISYE 6501 Midterm 1 exam with
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answers
Rows - CORRECT ANSWERS ✔✔Data points are values in data
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tables
Columns - CORRECT ANSWERS ✔✔The 'answer' for each data
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point (response/outcome)
|\
Structured Data - CORRECT ANSWERS ✔✔Quantitative,
|\ |\ |\ |\ |\ |\
Categorical, Binary, Unrelated, Time Series|\ |\ |\ |\
Unstructured Data - CORRECT ANSWERS ✔✔Text |\ |\ |\ |\ |\
Support Vector Model - CORRECT ANSWERS ✔✔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). |\ |\
What do you want to find with a SVM model? - CORRECT
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ANSWERS ✔✔Find values of a0, a1,...,up to am that classifies the
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points correctly and has the maximum gap or margin between the
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parallel lines. |\
What should the sum of the green points in a SVM model be? -
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CORRECT ANSWERS ✔✔The sum of green points should be
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greater than or equal to 1
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What should the sum of the red points in a SVM model be? -
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CORRECT ANSWERS ✔✔The sum of red points should be less
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than or equal to -1
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What should the total sum of green and red points be? - CORRECT
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ANSWERS ✔✔The total sum of all green and red points should be
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equal to or greater than 1 because yj is 1 for green and -1 for red.
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
First principal component - CORRECT ANSWERS ✔✔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. |\
Second principal component - CORRECT ANSWERS ✔✔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. |\ |\
What if it's not possible to separate green and red points in a SVM
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
model? - CORRECT ANSWERS ✔✔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. |\ |\ |\ |\
Soft Classifier - CORRECT ANSWERS ✔✔Account for errors in
|\ |\ |\ |\ |\ |\ |\ |\ |\
SVM classification. Trading off minimizing errors we make and
|\ |\ |\ |\ |\ |\ |\ |\ |\
maximizing the margin. |\ |\
|\ |\ |\ |\ |\ |\
answers
Rows - CORRECT ANSWERS ✔✔Data points are values in data
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\
tables
Columns - CORRECT ANSWERS ✔✔The 'answer' for each data
|\ |\ |\ |\ |\ |\ |\ |\ |\
point (response/outcome)
|\
Structured Data - CORRECT ANSWERS ✔✔Quantitative,
|\ |\ |\ |\ |\ |\
Categorical, Binary, Unrelated, Time Series|\ |\ |\ |\
Unstructured Data - CORRECT ANSWERS ✔✔Text |\ |\ |\ |\ |\
Support Vector Model - CORRECT ANSWERS ✔✔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). |\ |\
What do you want to find with a SVM model? - CORRECT
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
ANSWERS ✔✔Find values of a0, a1,...,up to am that classifies the
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
points correctly and has the maximum gap or margin between the
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
parallel lines. |\
What should the sum of the green points in a SVM model be? -
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
CORRECT ANSWERS ✔✔The sum of green points should be
|\ |\ |\ |\ |\ |\ |\ |\ |\
greater than or equal to 1
|\ |\ |\ |\ |\
What should the sum of the red points in a SVM model be? -
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
CORRECT ANSWERS ✔✔The sum of red points should be less
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\
than or equal to -1
|\ |\ |\ |\
What should the total sum of green and red points be? - CORRECT
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
ANSWERS ✔✔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.
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
First principal component - CORRECT ANSWERS ✔✔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. |\
Second principal component - CORRECT ANSWERS ✔✔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. |\ |\
What if it's not possible to separate green and red points in a SVM
|\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\ |\
model? - CORRECT ANSWERS ✔✔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. |\ |\ |\ |\
Soft Classifier - CORRECT ANSWERS ✔✔Account for errors in
|\ |\ |\ |\ |\ |\ |\ |\ |\
SVM classification. Trading off minimizing errors we make and
|\ |\ |\ |\ |\ |\ |\ |\ |\
maximizing the margin. |\ |\
