Which approach is generally used to investigate the robustness of a predictive model?

The use of cross-validation techniques is a standard approach to assess the robustness of a predictive model. This method involves partitioning the original dataset into several subsets, or folds, allowing the model to be trained and tested on different combinations of data. By doing this repeatedly, cross-validation provides a more comprehensive evaluation of the model's performance and its ability to generalize to unseen data. It helps to mitigate the risk of overfitting, where a model may perform well on the training dataset but poorly on new data, thus ensuring that the model's predictions are reliable.

Cross-validation techniques enable practitioners to obtain a better understanding of how the model will perform in real-world scenarios, as they simulate multiple training and testing environments using different portions of the data. The results from these multiple iterations can then be averaged to produce a robust estimate of the model's predictive power, contributing to a more validated and trustworthy model.