What is nested cross-validation and why is it important?

What is nested cross-validation and why is it important?

Nested cross-validation is a technique used to assess the performance of machine learning models. It involves two layers of cross-validation: the inner loop for hyperparameter tuning and the outer loop for model evaluation.

How does it differ from regular cross-validation?

In regular cross-validation, you typically split the data into training and testing sets once. Nested cross-validation, however, uses multiple splits to ensure that hyperparameter tuning does not bias the model evaluation.

Can you give me an example of how to implement it?

Sure! In Python, you can use libraries like scikit-learn. You would set up a GridSearchCV for the inner loop and then use cross_val_score for the outer loop.

What are the benefits of using nested cross-validation?

The main benefits include a more reliable estimate of model performance and reduced risk of overfitting, as it provides a more rigorous evaluation.