Post Hoc Selection

Post-hoc selection refers to choosing the best model or solution after the initial training or search process is complete, using performance metrics obtained from post-processing techniques or multiple objective functions. Current research focuses on improving model selection by leveraging post-hoc transformations like ensembling and analyzing Pareto frontiers in multi-objective optimization problems, developing algorithms that select the optimal model based on these post-hoc evaluations. This approach offers significant potential for enhancing model performance and robustness across diverse applications, particularly in scenarios with high noise or multiple conflicting objectives, leading to more reliable and effective machine learning systems.