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Day 14 - What is Unbalanced Data? / Six Techniques for Handling Unbalanced Data Quiz

Here’s your chance to prove what you learned

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Question 1 of 8

What characterizes an unbalanced dataset?

A

Equal distribution of all classes

B

One class significantly outnumbering others

C

Only two classes present in the data

D

All features having the same scale

Question 2 of 8

In the bank fraud detection example, which of the following represents the minority class?

A

Legitimate transactions

B

Fraudulent transactions

C

Bank accounts

D

Transaction amounts

Question 3 of 8

What is a primary challenge posed by unbalanced data in machine learning?

A

Increased computational cost

B

Difficulty in data collection

C

Model bias towards the majority class

D

Inability to use certain algorithms

Question 4 of 8

Why is addressing unbalanced data particularly important in fraud detection scenarios?

A

It makes the model run faster

B

It reduces the need for data collection

C

It ensures equal representation of all transaction types

D

It helps in accurately identifying rare but critical fraudulent cases

Question 5 of 8

Which of the following is NOT a resampling technique for handling unbalanced data?

A

Oversampling

B

Undersampling

C

SMOTE

D

Class Weighting

Question 6 of 8

What is the main difference between SMOTE and ROSE techniques?

A

SMOTE only works with numerical data, while ROSE works with categorical data

B

SMOTE generates exact copies of minority instances, while ROSE creates new variations

C

ROSE is only applicable to binary classification problems, while SMOTE works with multi-class problems

D

SMOTE is an undersampling technique, while ROSE is an oversampling technique

Question 7 of 8

In the context of unbalanced data, what does a hybrid approach refer to?

A

Combining supervised and unsupervised learning methods

B

Using both numerical and categorical features in a model

C

Applying both oversampling and undersampling techniques

D

Mixing different machine learning algorithms

Question 8 of 8

When would class weighting be particularly useful in handling unbalanced data?

A

When you want to create new synthetic examples

B

When you can't modify the original dataset

C

When dealing with time-series data

D

When you need to reduce the overall size of the dataset

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