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ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+ ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+ $11.99
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ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+ ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+

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ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+

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  • August 10, 2024
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ISYE 6501 - Midterm 1 Questions and
answers | Latest 2024/25 RATED A+
ii ii ii ii

What do descriptive questions ask?
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What happened? (e.g., which customers are most alike)
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What do predictive questions ask?
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What will happen? (e.g., what will Google's stock price be?)
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What do prescriptive questions ask?
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What action(s) would be best? (e.g., where to put traffic lights)
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What is a model? ii ii ii ii

Real-life situation expressed as math.
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What do classifiers help you do?
differentiate ii ii ii ii ii ii ii ii ii

What is a soft classifier and when is it used?
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In some cases, there won't be a line that separates all of the labeled examples. So we use a
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classifier that minimizes the number of mistakes.
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What does it mean when the classifier/decision boundary is almost parallel to the vertical x-
axis? ii ii ii ii ii ii ii

The horizontal attribute is all that is needed.
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What does it mean when the classifier/decision boundary is almost parallel to the
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horizontal y-axis?
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The vertical attribute is all that is needed.
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What is time-series data?
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The same data recorded over time often recorded at equal intervals
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What is quantitative data? ii ii ii ii ii ii ii ii ii ii ii ii

Number with a meaning: higher means more, lower means less (e.g., age, sales,
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temperature, income) ii ii ii

What is categorical data? ii ii ii ii ii ii ii ii ii ii ii ii

Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair color), binary data (e.g.,
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male/female, yes/no, on/off) ii ii ii ii ii ii

Which of these is time series data?
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A. The average cost of a house in the United States every year since 1820
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B. The height of each professional basketball player in the NBA at the start of the season
A ii ii ii ii ii

Which of these is structured data?
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A. The contents of a person's Twitter feed
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B. The amount of money in a person's bank account
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What is structured data?
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Data that can be stores in a structured way
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What is unstructured data?
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Data that is not easily described and stored (e.g., written text)
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A survey of 25 people recorded each person's family size and type of car. Which of these is
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a data point?

, ii ii ii ii ii ii ii ii

A. The 14th person's family size and car type
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B. The 14th person's family size ii ii ii ii ii

C.The car type of each person
A. ii ii ii ii ii ii ii ii ii

A data point is all the information about one observation
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The farther the wrongly classified point is from the line ___
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The bigger the mistake we've made
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The term including the margin gets larger so the importance of a large margin out weights
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avoiding mistakes and classifying known data samples.
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As lambda gets larger




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That term also drops towards zero, so the importance of minimizing mistakes and
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classifying known data points outweighs having a large margin.
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As lambda drops towards zero




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What can SVMs be used for
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to find a classifier with maximum seperation or margin between the two sets of points?
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When to use SVM?
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If it's impossible to avoid classification errors, SVM can find a classifier that trades off
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reducing errors and enlarging the margin.
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Error for data point j ii ii ii ii

What does this formula describe?




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Total error ii ii ii ii ii

What does this formula describe ?




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To maximize the distance between the two lines what do we need to minimize?

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m_j > 1 ii ii ii ii ii ii ii ii

What value do we give for more costly errors




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Giving a bad loan is twice as costly as withholding a good loan?
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What does this mean in the context of giving a loan?




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m_j < 1 ii ii ii ii ii ii ii ii

What value do we give for less costly errors?




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Why is it important to scale our data when using SVM?
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We're looking to minimize the sum of the squares of the coefficients, but if our data has very
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different scales a small change in one could swamp a huge change in the other.
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what does it signify when a coefficient for a classifier is close to zero
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it means the corresponding attribute is probably not relevant
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What do kernel methods allow for in SVMs ii

nonlinear classifiers ii ii ii ii ii ii ii

What is the common range for scaled data? ii ii ii

between 0 and 1 ii ii ii ii ii ii

What is the formula for min-max scaling?
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find min and max for a factor




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what is common standardization and its formula?
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scaling to a normal distribution with a mean of 0 and standard deviation of 1.

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