Cross Validation: What You Need To Know, From the Basics To LLMs

Cross Validation is a technique used in machine learning to evaluate the performance of predictive models by dividing the data into subsets, training the model on one subset, and testing it on another. This helps prevent overfitting and provides an unbiased estimate of the model's generalization error. There are various types of cross-validation techniques, including hold-out method, k-fold method, leave-p-out method, and rolling cross validation. Cross validation is especially important for large language models (LLMs) as it helps in tuning hyperparameters and ensuring that the model truly generalizes well to new examples.