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Day 7 - In-Sample vs. Out-of-Sample Quiz

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Question 1 of 4

 

What is the primary purpose of using out-of-sample data in machine learning?

A

To train the model more effectively

B

To increase the model's complexity

C

To test the model's performance on unseen data

D

To reduce the amount of data needed for training

Question 2 of 4

 

When is a machine learning model considered to be generalizing well?

A

When it performs well only on in-sample data

B

When it performs well only on out-of-sample data

C

When it performs well on both in-sample and out-of-sample data

D

When it performs poorly on both in-sample and out-of-sample data

Question 3 of 4

 

What does it typically indicate if a model performs well on in-sample data but poorly on out-of-sample data?

A

The model is underfitting

B

The model is overfitting

C

The model is generalizing well

D

The out-of-sample data is irrelevant

Question 4 of 4

 

In the analogy given in the lesson, what does the material covered in class represent?

A

Out-of-sample data

B

In-sample data

C

Model complexity

D

Overfitting

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