The holdout method is one of the simplest data science methods of mining data. The procedures are simple and take the shortest time because of the fewer variables involved in the process. For instance, there are two major data sets involved that are the training and testing data sets. During the data mining process, only the training data is applied. The testing data set acts as a control experiment compared based on the statistical model of fitting applied.
The results collected are compared with the test data set, and conclusions are recorded. The major aim of this process is for data variables comparison to mainly assess the association between variables of the study. The data applied is categorized into k subsets, and the method is also done repeatedly k times for results to be produced. Therefore, the comparison has made it easy for the data to be sorted and classified.
Cross-sectional methods are essential to machine learning methods. These methods are mainly used in the calculation and fixing the error rates. More especially, compared to the true error, estimating the rate of accuracy or bias in the process. The data set is split into two major parts, which creates the required information at the end of the analysis. Training and test data sets are used in this experiment to explore the data set patterns. In this method, the original data set is applied for easier comparison with the other methods. As such, the data has to be validated before it is used in the assessment.
In addition, several pieces of training must be done to ensure that there is no chance of bias in the analysis. However, the more data set training, many science tools have made this cross-sectional method better. Therefore, creating room for easy analysis and better results being collected.