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ISYE 6501 Midterm 1 already graded A+ 2024/2025
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ISYE 6501 Midterm 1 already graded A+ 2024/2025

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  • March 1, 2024
  • 10
  • 2023/2024
  • Exam (elaborations)
  • Questions & answers

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  • iqui
  • rows ansdata points are values in data tables c
  • what do you want to find with a svm model ansfi
  • first principal component anspca a linear com

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ISYE 6501 Midterm 1

Rows - ANSData points are values in data tables

Columns - ANSThe 'answer' for each data point (response/outcome)

Structured Data - ANSQuantitative, Categorical, Binary, Unrelated, Time Series

Unstructured Data - ANSText

Support Vector Model - ANSSupervised 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? - ANSFind 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? - ANSThe 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? - ANSThe sum of red points should
be less than or equal to -1

What should the total sum of green and red points be? - ANSThe 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 - ANSPCA -- 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 - ANSPCA -- 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? - ANSUtilize 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 - ANSAccount 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? - ANSYes, 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? - ANSIf 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? - ANSYes

Does a SVM classifier need to be a straight line? - ANSNo, 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? - ANSYes.

K Nearest Neighbor Algorithm - ANSFind the class of the new point, Pick the k closest points to
the new one, the new points class is the most common amongst the k neighbors.

What should you do about varying level of importance across attributes with K Nearest
Neighbors? - ANSSome attributes might be more important than others to the classification ---
can deal with this by weighting each dimension's distance differently.
Unimportant attributes may be removed as they are not very important for the classification.

What is the difference between real and random effects in validation? - ANSReal effects: same
in all data sets
Random effects: different in all data sets

How should one generally split their data set? - ANSTraining (building models) / Validation
(picking model) / Test (estimate performance)

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