LeaveOneOutvarianter
LeaveOneOutvarianter is a statistical technique used in model validation, particularly within machine learning. It is a specific type of cross-validation where, in each iteration, a single data point is held out as the validation set, and the remaining data is used for training. This process is repeated for every single data point in the dataset.
The core idea behind LeaveOneOutvarianter is to assess how well a model generalizes to unseen data by
The primary output of LeaveOneOutvarianter is an estimate of the model's error rate or predictive accuracy.