2025.01.27 What Are Repeats in Cross-Validation in R?

Structure of the Article

Introduction
What Are Repeats?
How Repeats Work in Cross-Validation
Advantages of Using Repeats
When to Use More or Fewer Repeats
Example in R
Conclusion
References

1. Introduction

Cross-validation is a key technique in machine learning for evaluating model performance. While folds divide the dataset into subsets, repeats determine how many times the cross-validation process is repeated with different random splits. In this blog, we’ll explore what repeats are, how they work, and how to use them effectively in your model evaluation.

2. What Are Repeats?

Repeats refer to the number of times the entire cross-validation process is repeated with different random splits of the data. For example, in 5-fold cross-validation with 3 repeats, the dataset is split into 5 folds, and the process is repeated 3 times with different random splits. The results are averaged to provide a more stable performance estimate.

3. How Repeats Work in Cross-Validation

The dataset is split into folds (e.g., 5 folds).
The model is trained and validated on each fold, and performance metrics are recorded.
This process is repeated multiple times (e.g., 3 repeats) with different random splits.
The final performance is the average of the results from all repeats.

Example of 5-Fold Cross-Validation with 3 Repeats:

Repeat 1: Split the data into 5 folds, train/validate, and record performance.
Repeat 2: Randomly split the data into 5 folds again, train/validate, and record performance.
Repeat 3: Randomly split the data into 5 folds once more, train/validate, and record performance.
Final Performance: The average of the results from all 3 repeats.

4. Advantages of Using Repeats

Reduces Variability: Provides a more stable estimate of model performance by averaging results across multiple splits.
Improves Reliability: Especially useful for small datasets or datasets with high variability.
Ensures Robustness: Helps avoid overfitting to a specific random split of the data.

5. When to Use More or Fewer Repeats

Use More Repeats (e.g., 5 or 10):

For small datasets to reduce variability in performance estimates.
When the dataset has high variability or imbalance.

Use Fewer Repeats (e.g., 3):

For large datasets where performance estimates are already stable.
When computational resources are limited.

6. Example in R

Here’s how to set up repeated cross-validation (5-fold with 3 repeats) using the caret package in R:

library(caret)

# Define trainControl for repeated cross-validation
train_control <- trainControl(
  method = "repeatedcv",      # Repeated cross-validation
  number = 5,                 # Number of folds
  repeats = 3                 # Number of repeats
)

# Train a model using repeated cross-validation
model <- train(
  Species ~ .,                # Formula: Predict Species using all other variables
  data = iris,                # Dataset
  method = "knn",             # Model type: K-Nearest Neighbors
  trControl = train_control   # Cross-validation configuration
)

# View the model results
print(model)

7. Conclusion

Repeats in cross-validation help reduce variability in performance estimates by repeating the process multiple times with different random splits of the data. This is especially useful for small datasets or datasets with high variability. By averaging the results across repeats, you can obtain a more stable and reliable estimate of your model’s performance.

Ready to try it out? Use the caret package in R to set up repeated cross-validation and improve your model evaluation today!

8. References

Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.
Link to Paper
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning.
Link to Book
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling.
Link to Book
Caret Package Documentation:
Caret Documentation
Scikit-learn Documentation on Cross-Validation:
Scikit-learn Cross-Validation