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

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

K-fold cross-validation can be computationally expensive, especially with large datasets. It may also lead to overfitting if the folds are not representative.

Can you explain why it might lead to overfitting?

If the data is not well shuffled or if there are outliers, some folds may not represent the overall dataset well, causing the model to perform poorly on unseen data.

Are there any alternatives to k-fold cross-validation?

Yes, alternatives include leave-one-out cross-validation, stratified k-fold, and simple train-test splits, each with its own pros and cons.

How do I choose the right cross-validation method for my dataset?

Consider the size of your dataset, the model complexity, and the computational resources available. Each method has trade-offs.