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What is agnostic model?
Interpretable models are models who explain themselves, for instance from a decision tree you can easily extract decision rules. Model-agnostic methods are methods you can use for any machine learning model, from support vector machines to neural networks.
How does a lime model work?
LIME provides local model interpretability. LIME modifies a single data sample by tweaking the feature values and observes the resulting impact on the output. Often, this is also related to what humans are interested in when observing the output of a model.
Why should I trust you explaining the predictions of any classifier 2016?
Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. …
What is local interpretable model agnostic explanation ( lime )?
We propose Local Interpretable Model-Agnostic Explanations (LIME), a technique to explain the predictions of any machine learning classifier, and evaluate its usefulness in various tasks related to trust.
Why do we need a local interpretable model?
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. This explanation is useless unless it is interpretable – that is, unless a human can make sense of it.
Why do we use lime in ML model?
The idea behind LIME is rather simple and elegant: for each individual instance that we pass to the model and for each prediction it makes we can always perform a “local sensitivity analysis“ in order to understand how sensitive is the prediction with regards to each feature of this particular instance.
What does lime mean in terms of interpretability?
First, a word about interpretability. Some classifiers use representations that are not intuitive to users at all (e.g. word embeddings). Lime explains those classifiers in terms of interpretable representations (words), even if that is not the representation actually used by the classifier.