What are common errors in k-fold cross-validation?
An example of an AI chatbot conversation about common errors in k-fold cross-validation.
What are common errors in k-fold cross-validation?
Some common errors include data leakage, improper shuffling, and not using enough folds.
Can you explain data leakage in this context?
Data leakage occurs when information from the test set is used to create the model, leading to overly optimistic performance.
How can I avoid data leakage during k-fold?
To avoid data leakage, ensure that the data is split into training and test sets before any preprocessing steps.
What is the impact of using too few folds?
Using too few folds can lead to high variance in the model evaluation, making it less reliable.
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