方法证据记录
LIME
LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.
源记录
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Local Interpretable Model-agnostic Explanations (LIME)
分类方法记录 · ml-model / machine-learning
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