ablation

A technique for evaluating the importance of a feature or component by temporarily removing it from a model. You then retrain the model without that feature or component, and if the retrained model performs significantly worse, then the removed feature or component was likely important. For example, suppose you train a classification model on 10 features and achieve 88% precision on the test set. To check the importance of the first feature, you can retrain the model using only the nine other features. If the retrained model performs significantly worse (for instance, 55% precision), then the removed feature was probably important. Conversely, if the retrained model performs equally well, then that feature was probably not that important. Ablation can also help determine the importance of: - Larger components, such as an entire subsystem of a larger ML system - Processes or techniques, such as a data preprocessing step In both cases, you would observe how the system's performance changes (or doesn't change) after you've removed the component.

Practitioners refer to ablation when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.