What are the advantages and disadvantages of k-fold cross-validation?

What are the advantages and disadvantages of k-fold cross-validation?

K-fold cross-validation helps in assessing the model's performance more reliably. Its advantages include better generalization and reduced bias. However, it can be computationally expensive and time-consuming.

Can you explain how it reduces bias?

Sure! By using multiple subsets of the data for training and validation, k-fold cross-validation ensures that every data point gets to be in both training and validation sets, which helps in providing a more accurate estimate of model performance.

What are some alternatives to k-fold cross-validation?

Alternatives include holdout validation, stratified sampling, and leave-one-out cross-validation. Each has its own pros and cons depending on the dataset and model.

Is k-fold suitable for all types of datasets?

Not necessarily. K-fold is generally suitable for larger datasets. For very small datasets, it might lead to overfitting, and other methods might be more appropriate.