What is k-fold cross-validation in deep learning?

What is k-fold cross-validation in deep learning?

K-fold cross-validation is a technique used to assess the performance of a model by dividing the dataset into k subsets. The model is trained on k-1 subsets and tested on the remaining subset, repeating this process k times.

Why is k-fold cross-validation important?

It helps in reducing overfitting, provides a better estimate of model performance, and ensures that every data point gets to be in both training and testing sets.

How do I choose the value of k?

A common choice for k is 5 or 10, but it can depend on the size of your dataset. Larger datasets can use a higher k, while smaller datasets might benefit from a lower k.

Can you explain the difference between k-fold and stratified k-fold?

Stratified k-fold ensures that each fold has the same proportion of classes as the entire dataset, which is particularly useful for imbalanced datasets.