ISYE 6501 Midterm 1 Targeted Questions
with Analyzed Answers
,Rows - CORRECT ANS:Data points are values in data tables
Columns - CORRECT ANS:The 'answer' for each data point (response/outcome)
Structured Data - CORRECT ANS:Quantitative, Categorical, Binary, Unrelated, Time Series
Unstructured Data - CORRECT ANS:Text
Support Vector Model - CORRECT ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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? - CORRECT ANS: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
How should you find which coefficients to hold value in a SVM model? - CORRECT ANS:If there is a
coefficient who's value is very close to 0, means the corresponding attribute is probably not relevant for
classification.
with Analyzed Answers
,Rows - CORRECT ANS:Data points are values in data tables
Columns - CORRECT ANS:The 'answer' for each data point (response/outcome)
Structured Data - CORRECT ANS:Quantitative, Categorical, Binary, Unrelated, Time Series
Unstructured Data - CORRECT ANS:Text
Support Vector Model - CORRECT ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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 ANS: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? - CORRECT ANS: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
How should you find which coefficients to hold value in a SVM model? - CORRECT ANS:If there is a
coefficient who's value is very close to 0, means the corresponding attribute is probably not relevant for
classification.
