Leave-One-Out Cross-Validation (LOOCV).
This is another version of k-fold cross validation where k = n, the number of data points. In this method, each time, only one data-point in the original dataset is held-out for model validation while the remaining data points are used to build the model. As a result, this process runs as many times as the number of data-points in the sample. This method provides negligible bias as the almost entire dataset is used for building the model, which is its advantage. However, this method has the major disadvantage that only one data point is used for validating the model every time, resulting in a high variance in the estimates of the model’s performance, particularly when multiple outliers in the dataset. In addition, this method is computationally very intensive, particularly when the dataset is large4.