What are the advantages of using k-fold cross-validation?

What are the advantages of using k-fold cross-validation?

K-fold cross-validation helps in assessing the model's performance more reliably by using different subsets of data for training and testing.

How does it compare to other validation methods?

Unlike a simple train-test split, k-fold uses multiple splits, which reduces variance and provides a better estimate of model performance.

Can you explain how to implement k-fold cross-validation?

Sure! You divide your dataset into k subsets, train the model k times, each time using a different subset as the test set.

What value of k is generally recommended?

Common choices for k are 5 or 10, but it can depend on the size of your dataset and the computational resources available.

Are there any drawbacks to using k-fold?

Yes, it can be computationally expensive, especially with large datasets, and may lead to overfitting if k is too large.