ISYE 6501 - Midterm 1 Questions with Verified
Correct Answers
Rows
Data points are values in data tables
Columns
The 'answer' for each data point (response/outcome)
Structured Data
Quantitative, Categorical, Binary, Unrelated, Time Series
Unstructured Data
Text
Support Vector Model
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?
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?
,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?
The sum of red points should be less than or equal to -1
What should the total sum of green and red points be?
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
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
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?
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
, 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?
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?
If there is a coefficient who's value is very close to 0, means the corresponding attribute is
probably not relevant for classification.
Does SVM work the same for multiple dimensions?
Yes
Does a SVM classifier need to be a straight line?
No, SVM can be generalized using kernel methods that allow for nonlinear classifiers.
Software has a kernel SVM function that you can use to solve for both linear and nonlinear
classifiers.
Can classification questions be answered as probabilities in SVM?
Correct Answers
Rows
Data points are values in data tables
Columns
The 'answer' for each data point (response/outcome)
Structured Data
Quantitative, Categorical, Binary, Unrelated, Time Series
Unstructured Data
Text
Support Vector Model
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?
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?
,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?
The sum of red points should be less than or equal to -1
What should the total sum of green and red points be?
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
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
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?
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
, 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?
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?
If there is a coefficient who's value is very close to 0, means the corresponding attribute is
probably not relevant for classification.
Does SVM work the same for multiple dimensions?
Yes
Does a SVM classifier need to be a straight line?
No, SVM can be generalized using kernel methods that allow for nonlinear classifiers.
Software has a kernel SVM function that you can use to solve for both linear and nonlinear
classifiers.
Can classification questions be answered as probabilities in SVM?
