Can K-Fold Validation Prevent Overfitting?

What is K-Fold validation and how does it work?

K-Fold validation is a technique used to assess the performance of a model. It involves splitting the dataset into 'K' subsets, or folds. The model is trained on K-1 folds and tested on the remaining fold. This process is repeated K times, with each fold serving as the test set once.

How does K-Fold validation help in preventing overfitting?

K-Fold validation helps in preventing overfitting by ensuring that the model is evaluated on different subsets of data. This provides a more reliable estimate of the model's performance and reduces the likelihood of the model fitting too closely to any single training set.

Are there any limitations to using K-Fold validation?

Yes, some limitations include increased computational cost, especially with large datasets, and the potential for variance in performance estimates if the folds are not representative of the overall dataset.

What is the ideal value for K in K-Fold validation?

The ideal value for K often depends on the size of the dataset. A common choice is 5 or 10, but for smaller datasets, a higher K can be beneficial to ensure enough training data.