An Analysis of Model Evaluation with Cross-Validation: Techniques, Applications, and Recent Advances

Cross-validation is the reuse of data, and then the acquired sample data is divided into different training sets and test sets. The model is trained with the training set, and the model forecast quality is evaluated by the test set. Based on these results, this paper can get several different training sets and test sets. A sample in a training set may become a sample in the next test set. This paper focuses on the three key cross-validation approaches. Recent advances such as nested cross-validation for model selection and time series cross-validation for sequence data are also discussed. For instance, in the area of home price forecasting, k-fold cross-validation ensures that the model performs reliably over different data segments, thus proving its robustness. Also, the geographical information data set is an example, in which the location of the data points is closer, the more dependent they are. By synthesizing insights from various studies, this review provides a comprehensive understanding of how cross-validation techniques can enhance model evaluation and guide the development of more accurate prediction models.