model-agnostic methods

Model-agnostic methods are techniques used to interpret and understand machine learning models regardless of the specific type or complexity of the model. Instead of relying on the internal mechanics, these methods analyze the model's outputs to identify which features influence its decisions most. For example, they might show how changing an input affects predictions or highlight important features. This approach helps ensure transparency and trust, especially when working with complex or "black box" models, by providing insights into how the model makes its choices without needing to alter or understand its internal structure.