Revision history [back]

K-fold cross-validation is a technique used for evaluating the performance of a machine learning model by dividing the dataset into K subsets or "folds" of equal size. The training process proceeds as follows:

1. Split the dataset into K subsets or "folds" of equal size.
2. For each fold k in K, train the model on all the subsets except the fold k, and evaluate the performance on the fold k.
3. Repeat step 2 K times, each time selecting a different fold k as the validation set.

Once the process is complete, average the evaluation metrics across all the K iterations to get an estimate of the model's performance. This technique helps to reduce the variance of the model by using multiple training and validation sets, and it also helps to prevent overfitting by using different subsets of the data for training and evaluation.

K-fold cross-validation can be used to tune hyperparameters, compare different models and algorithms, or to evaluate the general performance of a model on a given dataset. Overall, it is a useful technique for ensuring that a machine learning model is capable of generalizing well beyond the training data.