leave-one-out cross-validation

Leave-one-out cross-validation is a method used to evaluate how well a predictive model performs. Imagine having a dataset and wanting to test its accuracy. You take out one data point and train the model on the remaining data. Then, you use that model to predict the value of the left-out point. This process repeats for each data point in the dataset, each time leaving out a different one. By doing this, you can see how well the model predicts new, unseen data, providing a thorough assessment of its performance and generalizability.