Model Validation — K-fold Cross-validation

Exhibit 25.30 K-fold Cross-validation model.

K-fold cross-validation is a popular technique in machine learning for assessing a model's predictive performance. It involves dividing the dataset into k equal-sized subsets, or folds. The model is trained k times, each time using k-1 folds for training and the remaining fold for testing. This process is repeated k times, ensuring every fold serves as the test set exactly once.  

The results from each fold are averaged to provide a single performance estimate. This approach helps to reduce the variance in performance evaluation compared to a single train-test split, as the model is evaluated on multiple data combinations.

K-fold cross-validation is commonly used for:

• Obtaining a more reliable performance estimate: By reducing the variance associated with a single train-test split.
• Assessing generalization to unseen data: By evaluating the model on multiple subsets of the data.
• Identifying potential issues: Such as overfitting or underfitting, by analysing performance variation across different folds.

The choice of k (the number of folds) depends on factors like dataset size and computational resources. Common values include 5 or 10, but higher values might be used for larger datasets or when greater accuracy is desired.

For instance, with an 80-observation dataset and k=10, each fold would contain 8 observations. In each of the 10 iterations, one fold is used for testing, while the rest are used for training. This process is repeated 10 times, ensuring every fold serves as the test set once.