K-fold cross-validation is a technique used to assess the performance and reliability of a machine learning model. It involves dividing a dataset into K equal-sized subsets or “folds.” The model is trained and evaluated K times, with each iteration using a different fold as the validation set and the remaining K-1 folds for training. This process helps ensure that the model is tested on various portions of the data, reducing the risk of overfitting and providing a more robust evaluation of its generalization ability.
After each iteration, performance metrics (e.g., accuracy, error) are recorded. The final evaluation is typically the average of these metrics across all K iterations. K-fold cross-validation helps in estimating how well a model will perform on unseen data and aids in hyperparameter tuning. Common values for K are 5 or 10, but the choice depends on the size of the dataset and computational resources. Overall, K-fold cross-validation is a valuable tool for improving model reliability and making more informed decisions in machine learning.