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ISYE 6501 MIDTERM 1 | QUESTIONS AND ANSWERS
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ISYE 6501 MIDTERM 1 | QUESTIONS AND ANSWERS
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ISYE 6501 MIDTERM 1 | QUESTIONSAND 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? - Yes.
K Nearest Neighbor Algorithm - Find 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? - Some 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.
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