Leave One Out Cross Validation

Leave One Out Cross Validation (LOOCV) is a resampling technique used in machine learning and statistics to evaluate model performance. It involves training a model on all data points except one, testing it on the left-out point, and repeating this process for every data point in the dataset. This method provides an almost unbiased estimate of model error but can be computationally expensive for large datasets.

Also known as: LOOCV, Leave-One-Out, Leave-One-Out CV, Leave-One-Out Cross-Validation, LOO Cross Validation

Why learn Leave One Out Cross Validation?

Developers should use LOOCV when working with small datasets where data is scarce, as it maximizes training data usage and reduces bias in error estimation. It is particularly useful for model selection and hyperparameter tuning in scenarios like medical studies or experimental research with limited samples, where traditional k-fold cross-validation might not be feasible due to insufficient data splits.

Learning Resources

• docs: Scikit-learn Documentation: Leave-One-Out
• book: Introduction to Statistical Learning: Cross-Validation
• tutorial: Machine Learning Mastery: Leave-One-Out Cross-Validation
• course: Coursera: Machine Learning by Andrew Ng
• video: StatQuest: Cross Validation