Practice K-fold Cross-validation (28.3.2) - Introduction to Model Evaluation

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6 sample questions

Question 1.
What does K-Fold Cross-Validation do?
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Think about what it means to split data.
Question 2.
How many folds will be created if k=10?
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k is the number of folds.
3Question 3.
What is K-Fold Cross-Validation primarily used for?
- Data collection
- Model evaluation
- Data preprocessing
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Remember its purpose in helping assess models.
4Question 4.
True or False: In K-Fold Cross-Validation, the model is trained on the entire dataset once.
- True
- False
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Think about how many times the data is used.
Question 5.
You have a dataset of 1,000 samples, and you want to apply 10-Fold Cross-Validation. How would you set up your training and testing process?
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Think about how many rounds of training and testing you will do.
Question 6.
If you notice that model performance is highly inconsistent across different folds, what steps could you take to investigate the underlying issues?
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Consider what might cause uneven results across subsets.

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