What are some common errors in cross-validation?

What are some common errors in cross-validation?

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

Can you explain data leakage?

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?

To avoid data leakage, ensure that your training and test datasets are completely separate and that no future information is used during training.

What is stratified sampling?

Stratified sampling is a technique where the data is divided into different strata or groups, ensuring that each group is properly represented in both training and test sets.