Modelagnostic

Model-agnostic refers to methods, tools, or approaches that do not depend on the internal structure or parameters of a machine learning model. They treat the model as a black box and rely only on input-output behavior to analyze, explain, or optimize its predictions. This makes them broadly applicable across different model families, including linear models, tree ensembles, neural networks, and future algorithms.

Common use cases include explainability, debugging, fairness auditing, feature importance, and model comparison. Prominent model-agnostic methods include LIME (Local Interpretable Model-agnostic Explanations), Kernel SHAP (a model-agnostic approximation of Shapley values), and permutation feature importance, where features are permuted to observe impact on performance.

Technique categories range from local explanations (per-instance) to global approximations or diagnostic measures. Local surrogate models approximate a region around a prediction, while global methods aim to approximate overall model behavior through sampling and aggregation.

Strengths of model-agnostic approaches include broad applicability, support for proprietary or closed models, and the ability to operate without access to training data beyond features and outputs. Limitations include potential high query costs, reliance on sampling or perturbations, explanations that are approximate or unstable, and the possibility that they do not reflect causal relationships.

Relation to other concepts: model-agnostic methods contrast with model-specific approaches that use internal parameters or gradients, and they are a core part of the broader fields of interpretable machine learning and explainable AI.

See also: LIME, SHAP, feature importance.