Local interpretable model-agnostic explanations (LIME)[1] is a method that fits a surrogate glassbox model around the decision space of any blackbox models prediction.
Local Interpretable Model-Agnostic Explanations (LIME) the ELI5 way. Sumit Saha.In addition to it, the local model should be Model Agnostic, which basically means that it should treat the original model as a Black-Box, and hence be able to explain any model.
Lime is short for Local Interpretable Model-Agnostic Explanations. Each part of the name reflects something that we desire in explanations. Local refers to local fidelity - i.e., we want the explanation to really reflect the behaviour of the classifier "around" the instance being predicted.
As we can see from the illustration, Local Interpretable Model-Agnostic Explanations has many fascinating aspects to explore.
Lime stands for Local, Interpretable Model-agnostic Explanations and is a technique proposed by Ribeiro et al. in 2016.
This chapter introduces Local Interpretable Model-agnostic Explanations (LIME), a technique designed specifically for this purpose. LIME works by approximating the complex model locally around the prediction you want to explain using a simpler, interpretable model.
Local Interpretable Model-Agnostic Explanations (LIME) works by generating explanations for individual predictions made by any machine learning model. It creates a simpler, interpretable model (e.g., linear classifier) around the prediction...
LIME (Local Interpretable Model-Agnostic Explanations) is a method that aims at explaining the predictions of any machine learning classifier in a way that humans can understand.
