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. It involves splitting the dataset into 'k' subsets or folds.

How does the process work?

In k-fold cross-validation, 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.

What are the benefits of using k-fold cross-validation?

It helps in reducing overfitting, provides a better estimate of model performance, and utilizes the entire dataset for both training and testing.

Are there any drawbacks to this method?

Yes, it can be computationally expensive, especially with large datasets, and may not be suitable for time-series data.