When should I use K-Fold cross-validation?

What is K-Fold cross-validation and when should I use it?

K-Fold cross-validation is a technique to assess the performance of a model by dividing the dataset into K subsets. You should use it when you want to ensure that your model generalizes well to unseen data.

How do I choose the value of K?

The value of K is typically chosen based on the size of your dataset. A common choice is 5 or 10, but for smaller datasets, you might use a higher K to ensure each fold has enough data.

Are there any drawbacks to using K-Fold?

Yes, K-Fold can be computationally expensive, especially with large datasets, as it requires training the model K times. Additionally, if the data is not shuffled properly, it may lead to biased results.

Can I use K-Fold for time series data?

K-Fold is not ideal for time series data due to the temporal dependencies. Instead, consider using techniques like Time Series Split.