Final answer:
K-fold cross validation is a technique used to evaluate the performance of machine learning models. It involves splitting the available data into K subsets or folds and repeating the training and testing process K times.
Step-by-step explanation:
K-fold cross validation is a technique used to evaluate the performance of machine learning models. It involves splitting the available data into K subsets or folds. The model is trained on K-1 subsets and tested on the remaining fold. This process is repeated K times, each time using a different fold as the testing set. For example, let's say we have a dataset of 1000 samples and we choose K=5. In the first iteration, the model is trained on 800 samples and tested on 200 samples. In the second iteration, the model is trained on a different 800 samples and tested on a different 200 samples. This process is repeated 5 times, and at the end, we would have used all of the 1000 samples for both training and testing. K-fold cross validation helps address the issue of overfitting and provides a more reliable estimate of a model's performance. It allows us to assess how well the model generalizes to unseen data. Additionally, it helps in selecting the best hyperparameters or tuning the model.