100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
logo-home
ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+ ISYE 6501 - Midterm 1 Questions and answers | Latest 2024/25 RATED A+ £9.42   Add to cart

Exam (elaborations)

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

 3 views  0 purchase
  • Module
  • ISYE 6501
  • Institution
  • ISYE 6501

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

Preview 3 out of 18  pages

  • August 10, 2024
  • 18
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • ISYE 6501
  • ISYE 6501
avatar-seller
ISYE 6501 - Midterm 1 Questions and
answers | Latest 2024/25 RATED A+
ii ii ii ii

What do descriptive questions ask?
ii ii ii ii ii ii ii

What happened? (e.g., which customers are most alike)
ii ii ii ii

What do predictive questions ask?
ii ii ii ii ii ii ii ii ii

What will happen? (e.g., what will Google's stock price be?)
ii ii ii ii

What do prescriptive questions ask?
ii ii ii ii ii ii ii ii ii ii

What action(s) would be best? (e.g., where to put traffic lights)
ii ii ii

What is a model? ii ii ii ii

Real-life situation expressed as math.
ii ii ii ii ii

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?
ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii

In some cases, there won't be a line that separates all of the labeled examples. So we use a
ii ii ii ii ii ii ii

classifier that minimizes the number of mistakes.
ii ii ii ii ii ii ii ii ii ii ii ii ii ii

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.
ii ii ii ii ii ii ii ii ii ii ii ii

What does it mean when the classifier/decision boundary is almost parallel to the
ii ii

horizontal y-axis?
ii ii ii ii ii ii ii

The vertical attribute is all that is needed.
ii ii ii

What is time-series data?
ii ii ii ii ii ii ii ii ii ii

The same data recorded over time often recorded at equal intervals
ii ii ii

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,
ii ii

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.,
ii ii ii

male/female, yes/no, on/off) ii ii ii ii ii ii

Which of these is time series data?
ii ii ii ii ii ii ii ii ii ii ii ii ii ii

A. The average cost of a house in the United States every year since 1820
ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii

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?
ii ii ii ii ii ii ii

A. The contents of a person's Twitter feed
ii ii ii ii ii ii ii ii ii

B. The amount of money in a person's bank account
B ii ii ii

What is structured data?
ii ii ii ii ii ii ii ii

Data that can be stores in a structured way
ii ii ii

What is unstructured data?
ii ii ii ii ii ii ii ii ii ii

Data that is not easily described and stored (e.g., written text)
ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii

A survey of 25 people recorded each person's family size and type of car. Which of these is
ii ii ii

a data point?

, ii ii ii ii ii ii ii ii

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

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
ii ii ii ii ii ii ii ii ii ii

The farther the wrongly classified point is from the line ___
ii ii ii ii ii

The bigger the mistake we've made
ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii

The term including the margin gets larger so the importance of a large margin out weights
ii ii ii ii ii ii ii

avoiding mistakes and classifying known data samples.
ii ii ii

As lambda gets larger




ii ii ii ii ii ii ii ii ii ii ii ii

That term also drops towards zero, so the importance of minimizing mistakes and
ii ii ii ii ii ii ii ii ii

classifying known data points outweighs having a large margin.
ii ii ii ii

As lambda drops towards zero




ii ii ii ii ii

What can SVMs be used for
ii ii ii ii ii ii ii ii ii ii ii ii ii ii

to find a classifier with maximum seperation or margin between the two sets of points?
ii ii ii

When to use SVM?
ii ii ii ii ii ii ii ii ii ii ii ii ii ii

If it's impossible to avoid classification errors, SVM can find a classifier that trades off
ii ii ii ii ii ii

reducing errors and enlarging the margin.
ii ii ii ii

Error for data point j ii ii ii ii

What does this formula describe?




ii

Total error ii ii ii ii ii

What does this formula describe ?




ii ii ii ii ii ii ii ii ii ii ii ii ii

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

, ii ii

m_j > 1 ii ii ii ii ii ii ii ii

What value do we give for more costly errors




ii ii ii ii ii ii ii ii ii ii ii ii

Giving a bad loan is twice as costly as withholding a good loan?
ii ii ii ii ii ii ii ii ii ii

What does this mean in the context of giving a loan?




ii ii

m_j < 1 ii ii ii ii ii ii ii ii

What value do we give for less costly errors?




ii ii ii ii ii ii ii ii ii ii

Why is it important to scale our data when using SVM?
ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii

We're looking to minimize the sum of the squares of the coefficients, but if our data has very
ii ii ii ii ii ii ii ii ii ii ii ii ii ii ii

different scales a small change in one could swamp a huge change in the other.
ii ii ii ii ii ii ii ii ii ii ii ii ii

what does it signify when a coefficient for a classifier is close to zero
ii ii ii ii ii ii ii ii

it means the corresponding attribute is probably not relevant
ii ii ii ii ii ii ii

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?
ii ii ii ii ii ii

find min and max for a factor




ii ii ii ii ii ii

what is common standardization and its formula?
ii ii ii ii ii ii ii ii ii ii ii ii ii ii

scaling to a normal distribution with a mean of 0 and standard deviation of 1.

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller STUVATE. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for £9.42. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

77333 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy revision notes and other study material for 14 years now

Start selling
£9.42
  • (0)
  Add to cart