In machine learning (ML), cross validation is a method in which the data scientists perform an evaluation of an ML model's performance on unlabelled data, i.e. data which the ML model has not seen before. In the method of cross validation, the data which is available in the dataset is split into multiple subsets. One ... Read more
In machine learning, k-fold cross validation is a type of cross validation in which the dataset is split into k subsets and the validation process is repeated k times. Each time k-1 subsets are used for training the ML model, while one (1) subset is used for testing/validation. The final validation generalization capability/performance of the ... Read more
In machine learning, LOOCV stands for leave-one-out cross validation. Assuming that the dataset of an ML project comprises n examples (rows), we split the dataset into n subsets and perform n iterations of training/testing. In each iteration, n-1 examples are used as training data and only 1 example is used the testing subset. This process ... Read more
