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Question 1 of 4
To train the model more effectively
To increase the model's complexity
To test the model's performance on unseen data
To reduce the amount of data needed for training
Question 2 of 4
When it performs well only on in-sample data
When it performs well only on out-of-sample data
When it performs well on both in-sample and out-of-sample data
When it performs poorly on both in-sample and out-of-sample data
Question 3 of 4
The model is underfitting
The model is overfitting
The model is generalizing well
The out-of-sample data is irrelevant
Question 4 of 4
Out-of-sample data
In-sample data
Model complexity
Overfitting