K-Fold Cross-Validation Purpose

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You have a dataset of 5,000 samples and want a reliable estimate of your model's performance. Why would you use 5-fold cross-validation instead of a single train/test split?

Multiple Choice

It trains the model 5 times faster than a single split It ensures every data point is used for both training and validation, reducing variance in the performance estimate It increases the size of the training set by 5x It eliminates the need for a held-out test set entirely

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K-Fold Cross-Validation Purpose

Multiple Choice

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