Contents
What is lime in deep learning?
LIME is model-agnostic, meaning that it can be applied to any machine learning model. The technique attempts to understand the model by perturbing the input of data samples and understanding how the predictions change.
What is lime in NLP?
At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations).
What is lime package?
‘lime’ (a port of the ‘lime’ ‘Python’ package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al.
Is Lime a surrogate model?
Instead of training a global surrogate model, LIME trains local surrogate models to explain individual predictions.
What is lime in AI?
LIME, the acronym for local interpretable model-agnostic explanations, is a technique that approximates any black box machine learning model with a local, interpretable model to explain each individual prediction. Each original data point can then be explained with the newly trained explanation model.
Is Lime a juice?
A lime (from French lime, from Arabic līma, from Persian līmū, “lemon”), is a citrus fruit, which is typically round, green in color, 3–6 centimetres (1.2–2.4 in) in diameter, and contains acidic juice vesicles. Plants with fruit called “limes” have diverse genetic origins; limes do not form a monophyletic group.
Which is better lime or Shap?
LIME and SHAP are both good methods for explaining models. In theory, SHAP is the better approach as it provides mathematical guarantees for the accuracy and consistency of explanations. In practice, the model agnostic implementation of SHAP (KernelExplainer) is slow, even with approximations.
What kind of explanations are available for lime?
Further, lime takes human limitations into account: i.e. the explanations are not too long. Right now, our package supports explanations that are sparse linear models (as presented before), although we are working on other representations.
How to create a lime Explainer in HTML?
The user enters a piece of text, selects the number of random samples to generate for LIME, and then chooses from a given list of classifiers using the dropdown menu. Clicking on the Explain results! button then generates a LIME explanation HTML object, which is rendered in an HTML Iframe.
How does explain your model with Shap and lime?
At a high level, the way both of these work is that you give your training data and model to an “explainer”, and then you’re later able to pass any observation to the “explainer” which will tell you the feature importance of that model. That might sound like gobbledegook right now, but we’ll make this extra clear with our Titanic example!
How to get explanations for image classifiers in lime?
Getting explanations for image classifiers is something that is not yet available in the lime package, but we are working on it. Lime is short for Local Interpretable Model-Agnostic Explanations. Each part of the name reflects something that we desire in explanations.