Ablation Study
A systematic evaluation process where individual components of a machine learning model are removed to measure their exact contribution to performance.
Think of It Like This
Like removing ingredients from a recipe one by one to figure out exactly which spice is making the soup taste good.
By systematically disabling features, layers, or loss terms, researchers isolate which parts of a complex system actually drive improvements. This is crucial for avoiding bloated architectures where useless components are kept just because they were part of a successful run. A rigorous ablation study proves that every remaining piece of the model is doing necessary work.