2025.01.27 How to Decide the Number of Folds and Repeats in Cross-Validation in R?

Structure of the Article

Introduction
Number of Folds (number)
Number of Repeats (repeats)
Practical Considerations
Example Configuration in R
When to Adjust Folds and Repeats
Why This Matters
Conclusion
References

1. Introduction

Cross-validation is a fundamental technique in machine learning for evaluating model performance and ensuring generalizability. However, the reliability of your results depends heavily on how you set up your cross-validation process. One of the key decisions is choosing the number of folds (number) and repeats (repeats). In this blog, we’ll explore how to make these choices effectively, considering factors like dataset size, computational resources, and the balance between bias and variability.

2. Number of Folds (`number`)

Common Choice: 5 or 10 folds are commonly used in practice.
Small Datasets: For smaller datasets, use a higher number of folds (e.g., 10) to ensure that each fold has enough data for training and validation.
Large Datasets: For larger datasets, fewer folds (e.g., 5) can be sufficient, as the model will have enough data in each fold to generalize well.
Trade-off: Increasing the number of folds reduces bias in the performance estimate but increases variance and computational cost.

3. Number of Repeats (`repeats`)

Common Choice: 3 to 5 repeats are typical for repeated cross-validation.
Purpose: Repeating cross-validation helps reduce the variability in performance estimates caused by the randomness of data splitting.
Small Datasets: For smaller datasets, more repeats (e.g., 5) can provide a more stable estimate of model performance.
Large Datasets: For larger datasets, fewer repeats (e.g., 3) may be sufficient, as the performance estimates are already stable.

4. Practical Considerations

Computational Resources: More folds and repeats increase computational time. Ensure your system can handle the chosen configuration.
Dataset Size: For very small datasets, consider using Leave-One-Out Cross-Validation (LOOCV) or stratified sampling to ensure each fold has a representative sample of the data.
Model Stability: If the model performance varies significantly across folds, increasing the number of repeats can help stabilize the estimate.

5. Example Configuration in R

Here’s how to set up cross-validation in R using the caret package:

library(caret)

# Define trainControl for cross-validation
train_control <- trainControl(
  method = "repeatedcv",      # Repeated cross-validation
  number = 10,                # 10 folds (common choice for small to medium datasets)
  repeats = 5,                # 5 repeats (to stabilize performance estimates)
  savePredictions = "final"   # Save predictions for the final model
)

# Save train_control object to an RDS file
saveRDS(train_control, "./train_control_config.Rds")

# Load train_control object from the RDS file (if needed later)
train_control <- readRDS("./train_control_config.Rds")

6. When to Adjust Folds and Repeats

Increase Folds: If you suspect high bias in performance estimates or have a small dataset.
Increase Repeats: If you observe high variability in performance estimates across folds.
Decrease Folds/Repeats: If computational time is a concern or the dataset is very large.

7. Why This Matters

Avoid Overfitting: Cross-validation provides a more reliable estimate of model performance by testing on multiple subsets of the data.
Ensure Reproducibility: Saving the cross-validation configuration allows you to reuse the same settings in future analyses.
Improve Model Selection: Cross-validation helps you choose the best model by comparing performance across different configurations.

8. Conclusion

By carefully selecting the number of folds and repeats, you can achieve a robust and reliable evaluation of your model’s performance. Whether you’re working with small or large datasets, understanding these concepts will help you set up cross-validation effectively.

Ready to try it out? Install the caret package and start setting up cross-validation in your projects today!

install.packages("caret")
library(caret)

9. References

Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning.
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling.
Caret Package Documentation: https://topepo.github.io/caret/