Cross-validation can be defined as the use of one or more statistical techniques to validate the reliability of the prediction of the model. Typically, cross-validation is used in the case of the small datasets which is difficult i.e. splitting the data into two parts does not result in good prediction.
As mentioned above, cross-validation is accomplished by partitioning your data. The procedure of cross-validation is repeated until each partition has been utilised as test data and as training data twice. After cross-validation is complete, you will see a visualisation demonstrating your model’s ability to predict properly on previously unknown data.
There are several types of cross-validation methods; for example, k-fold cross-validation divides all available cases into k subsets, each of which contains approximately half the number of observations. Repeating this process ten times results in ten accuracy ratios, which can then be averaged for cross-validation accuracy assessment.
Let’s n be the number of data points in the training dataset. Let’s k be an integer index that is much smaller than n.
In k-fold cross-validation, we divide the entire data set into k equal-size data subsets and use k-1 part for the training and the remaining part for testing and calculation of the prediction error.
We repeat the procedure k times and report the average from k-runs. This method is frequently used in reporting the results in the literature as 10-fold cross-validation, where the data set is divided into 10 subsets and the final prediction error is calculated as 1/10 times the sum of the ten errors.
Cross-validation in data mining may be found via an online search. Cross-validation may be integrated into certain statistical packages, or you may be able to locate a publicly available cross-validation code online. Numerous open-source cross-validation tools are available, including Folds, Cross Validated Fligner – Karp, and simplecv. Additionally, there are websites devoted to algorithm cross-validation testing, such as cross-validated and CVaRude.