Train-Validation-Test Split vs Nested Cross Validation

Developers should use this split when building any supervised machine learning model to avoid data leakage and over-optimistic performance estimates meets developers should use nested cross validation when building machine learning models that require hyperparameter tuning, especially in scenarios with limited data or high risk of overfitting. Here's our take.

Train-Validation-Test Split

Nice Pick

Developers should use this split when building any supervised machine learning model to avoid data leakage and over-optimistic performance estimates

Pros

• It's essential for hyperparameter tuning (using the validation set) and final unbiased evaluation (using the test set), particularly in projects with limited data or high-stakes applications like healthcare or finance
• Related to: cross-validation, hyperparameter-tuning

Cons

• Specific tradeoffs depend on your use case

Nested Cross Validation

Developers should use Nested Cross Validation when building machine learning models that require hyperparameter tuning, especially in scenarios with limited data or high risk of overfitting

Pros

• It is essential for ensuring fair comparisons between different models or algorithms, such as in research papers, Kaggle competitions, or production systems where accurate performance metrics are critical
• Related to: cross-validation, hyperparameter-tuning

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Train-Validation-Test Split if: You want it's essential for hyperparameter tuning (using the validation set) and final unbiased evaluation (using the test set), particularly in projects with limited data or high-stakes applications like healthcare or finance and can live with specific tradeoffs depend on your use case.

Use Nested Cross Validation if: You prioritize it is essential for ensuring fair comparisons between different models or algorithms, such as in research papers, kaggle competitions, or production systems where accurate performance metrics are critical over what Train-Validation-Test Split offers.

The Bottom Line

Train-Validation-Test Split wins

Developers should use this split when building any supervised machine learning model to avoid data leakage and over-optimistic performance estimates