Rekodi ya ushahidi wa mbinu

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.

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Rekodi ya chanzo

Nukuu zimehamishwa kwa uhalisi kutoka kwa rekodi ya chanzo cha mbinu. Hakuna uthibitisho wa kiwango cha dai unaodokezwa kutoka kwao.

Local Interpretable Model-agnostic Explanations (LIME)

Rekodi ya mbinu ya kiajenda · ml-model / machine-learning

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144. · DOI 10.1145/2939672.2939778

Fungua mbinu kamili

Madai yaliyotunzwa

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Hakuna madai yaliyotunzwa bado

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Mbinu zinazohusiana

Zilizotengenezwa kutoka kwa grafu ya mbinu na kuonyeshwa kama uhusiano uliopendekezwa na mashine — hakuna dai la ushahidi linalodokezwa.

Taxonomic bucketCounterfactual Explanationsmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.