What is K-Fold Cross-Validation?

Can you explain what K-Fold Cross-Validation is?

Sure! K-Fold Cross-Validation is a technique used to assess the performance of a machine learning model by dividing the dataset into K subsets.

How does it work in practice?

In practice, the dataset is split into K equal parts. The model is trained on K-1 parts and tested on the remaining part. This process is repeated K times.

What are the benefits of using K-Fold Cross-Validation?

The main benefits include better model evaluation, reduced overfitting, and more reliable performance metrics.

Are there any drawbacks to this method?

Yes, it can be computationally expensive, especially with large datasets, and may not be suitable for very small datasets.