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Answer: Cross-validation provides a more reliable estimate of model performance on unseen data by averaging results over multiple splits.
Cross-validation involves partitioning the data into subsets and training the model on different combinations of these subsets, which helps in obtaining a more robust estimate of model performance. However, it can be computationally expensive and may not always be necessary or beneficial, especially with large datasets or when computational resources are limited.
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Explain the concept of cross-validation in detail and discuss its advantages and disadvantages compared to a simple train-validation split. Consider scenarios where cross-validation might be particularly beneficial or detrimental.
A
Cross-validation is always superior to train-validation split in all scenarios.
B
Cross-validation can lead to overfitting in small datasets.
C
Cross-validation provides a more reliable estimate of model performance on unseen data by averaging results over multiple splits.
D
Cross-validation is less computationally expensive than train-validation split.
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