LOOCV for Evaluating Machine Learning Algorithms

The Leave-One-Out Cross-Validation, or LOOCV, procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model. It is a computationally expensive procedure to perform, although it results in a reliable and unbiased estimate of model performance. Although simple to use and no configuration to specify, there are times when the procedure should not be used, such as when you have a very large dataset or a computationally expensive model to evaluate. In this tutorial, you will discover how to evaluate machine learning models using leave-one-out cross-validation. LOOCV for Evaluating Machine Learning Algorithms Photo by Heather Harvey, some rights reserved.