Model agnostic methods refer to techniques that can be applied to any predictive model, regardless of its underlying architecture or assumptions. These methods allow for the interpretation, evaluation, and improvement of models without needing to understand the internal mechanics. This is particularly useful in nanotechnology, where models can range from simple linear regressions to complex deep learning architectures.
