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AI-woordenlijst

Het complete woordenboek van kunstmatige intelligentie

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Anchors

A method that identifies simple and sufficient decision rules (anchors) that explain a model's prediction for a given instance with high fidelity.

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Shapley Value

A concept from game theory that measures the average marginal contribution of a player (feature) across all possible coalitions, serving as the foundation for SHAP.

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Input Perturbation

The process of creating slight variations in the input data to observe the effect on the model's prediction, used by methods like LIME to build a local neighborhood.

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Fidelity

A metric evaluating how faithfully a local explanation (like LIME's simple model) mimics the behavior of the complex model in its neighborhood.

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Model-Agnostic Explanation

An interpretability approach that treats the predictive model as a black box, interacting only with its inputs and outputs to generate explanations.

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Saliency Map

A visualization that highlights the pixels or features of an input that most influenced a model's prediction, often obtained by computing the gradient.

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Kernel Neighborhood

In LIME, a function that defines the proximity between the original instance and the perturbed instances, weighting their influence in the local explanation model.

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Explanation Rule

A simple logical condition (e.g., IF feature_A > X AND feature_B < Y) that captures the primary reason for a specific prediction, typical of methods like Anchors.

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Post-hoc Interpretability

The analysis of a model after its training to understand its decisions, as opposed to intrinsically interpretable models.

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SHAP Kernel Explainer

A SHAP implementation using kernel weighting to estimate Shapley values, making it model-agnostic but potentially slower.

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SHAP Tree Explainer

An optimized SHAP algorithm that calculates exact Shapley values for tree-based models (like XGBoost, LightGBM) very efficiently.

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Local Surrogate Explanation

The fundamental principle of LIME, consisting of training a simple and interpretable model (surrogate) to approximate the behavior of the complex model locally.

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