How does k-fold cross-validation work in model evaluation?

How does k-fold cross-validation work in model evaluation?

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. This process is repeated 'k' times, with each subset used as the test set once.

What are the advantages of using k-fold over a simple train-test split?

K-fold cross-validation provides a more reliable estimate of model performance by using multiple train-test splits. It reduces the variance associated with a single train-test split and ensures that every data point gets to be in both training and test sets.

How do I choose the value of k?

The value of k is often chosen based on the size of the dataset. A common choice is 5 or 10, but for smaller datasets, you might use a value closer to the number of samples.

Can k-fold cross-validation be used for all types of models?

Yes, k-fold cross-validation can be applied to any model, including regression, classification, and even complex models like neural networks.