ISYE 6501 MIDTERM 1 UPDATED ACTUAL
QUESTIONS AND CORRECT ANSWERS
◉ Columns.
Answer: The 'answer' for each data point (response/outcome)
◉ Structured Data.
Answer: Quantitative, Categorical, Binary, Unrelated, Time Series
◉ Unstructured Data.
Answer: Text
◉ 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).
,◉ 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.
◉ 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
◉ 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
◉ What should the total sum of green and red points be?.
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.
◉ 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.
◉ 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.
◉ 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.
◉ 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?.
QUESTIONS AND CORRECT ANSWERS
◉ Columns.
Answer: The 'answer' for each data point (response/outcome)
◉ Structured Data.
Answer: Quantitative, Categorical, Binary, Unrelated, Time Series
◉ Unstructured Data.
Answer: Text
◉ 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).
,◉ 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.
◉ 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
◉ 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
◉ What should the total sum of green and red points be?.
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.
◉ 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.
◉ 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.
◉ 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.
◉ 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?.
