Model-Agnostic

Topic

In machine learning and data science, model-agnostic refers to methods, algorithms, or tools that can be applied to any machine learning model regardless of its internal architecture. These approaches treat the underlying model as a black box, analyzing only its inputs and outputs to perform tasks such as model interpretation, meta-learning, or evaluation. Common examples include model-agnostic explanation methods like LIME and SHAP, which explain predictions without needing access to the model's internal weights or parameters.