cross-validation, sometimes called rotation estimation, is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. it is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. in a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (testing dataset). the goal of cross validation is to define a dataset to "test" the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent data set (i.e., an unknown dataset, for instance from a real problem), etc. one round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). to reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. cross-validation is important in guarding against testing hypotheses suggested by the data (called "type iii errors"), especially where further samples are hazardous, costly or impossible to collect. ==purpose of cross validation== suppose we have a model with one or more unknown parameters, and a data set to which the model can be
Machine_learning Model_selection Regression_variable_selection