LIME, or Local Interpretable Model-agnostic Explanations, is a technique for understanding machine learning predictions. It works by perturbing input data to see how a model's output changes, creating a simple, interpretable model around a single prediction. Data scientists and ML engineers use it to debug models, while business stakeholders and regulators benefit from greater transparency, building trust in AI decisions. It clarifies why a specific decision was made, making AI more accountable.
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