Data Science: Cross-validation (Definitions)

"A method for assessing the accuracy of a regression or classification model. A data set is divided up into a series of test and training sets, and a model is built with each of the training set and is tested with the separate test set." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A method for assessing the accuracy of a regression or classification model." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"A statistical method derived from cross-classification which main objective is to detect the outlying point in a population set." (Tomasz Ciszkowski & Zbigniew Kotulski, "Secure Routing with Reputation in MANET", 2008)

"Process by which an original dataset d is divided into a training set t and a validation set v. The training set is used to produce an effort estimation model (if applicable), later used to predict effort for each of the projects in v, as if these projects were new projects for which effort was unknown. Accuracy statistics are then obtained and aggregated to provide an overall measure of prediction accuracy." (Emilia Mendes & Silvia Abrahão, "Web Development Effort Estimation: An Empirical Analysis", 2008)

"A method of estimating predictive error of inducers. Cross-validation procedure splits that dataset into k equal-sized pieces called folds. k predictive function are built, each tested on a distinct fold after being trained on the remaining folds." (Gilles Lebrun et al, EA Multi-Model Selection for SVM, 2009)

"Method to estimate the accuracy of a classifier system. In this approach, the dataset, D, is randomly split into K mutually exclusive subsets (folds) of equal size (D1, D2, …, Dk) and K classifiers are built. The i-th classifier is trained on the union of all Dj ¤ j¹i and tested on Di. The estimate accuracy is the overall number of correct classifications divided by the number of instances in the dataset." (M Paz S Lorente et al, "Ensemble of ANN for Traffic Sign Recognition" [in "Encyclopedia of Artificial Intelligence"], 2009)

"The process of assessing the predictive accuracy of a model in a test sample compared to its predictive accuracy in the learning or training sample that was used to make the model. Cross-validation is a primary way to assure that over learning does not take place in the final model, and thus that the model approximates reality as well as can be obtained from the data available." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Validating a scoring procedure by applying it to another set of data." (Dougal Hutchison, "Automated Essay Scoring Systems", 2009)

"A method for evaluating the accuracy of a data mining model." (Microsoft, "SQL Server 2012 Glossary", 2012)

"Cross-validation is a method of splitting all of your data into two parts: training and validation. The training data is used to build the machine learning model, whereas the validation data is used to validate that the model is doing what is expected. This increases our ability to find and determine the underlying errors in a model." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"A technique used for validation and model selection. The data is randomly partitioned into K groups. The model is then trained K times, each time with one of the groups left out, on which it is evaluated." (Simon Rogers & Mark Girolami, "A First Course in Machine Learning", 2017)

"A model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set." (Adrian Carballal et al, "Approach to Minimize Bias on Aesthetic Image Datasets", 2019)