What are some common errors in k-fold cross-validation?

What are some common errors in k-fold cross-validation?

Some common errors include data leakage, improper shuffling, and not using stratified sampling.

Can you explain data leakage in more detail?

Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance.

How can I avoid data leakage during k-fold cross-validation?

To avoid data leakage, ensure that your data is properly split and that no information from the validation set is used in training.

What is stratified sampling and why is it important?

Stratified sampling ensures that each fold has a representative distribution of classes, which is crucial for imbalanced datasets.