# LIME¶

## Algorithm¶

LIME (Ribeiro et. al. 2016) is an algorithm to explain predictions of black-box estimators:

1. Generate a fake dataset from the example we’re going to explain.

2. Use black-box estimator to get target values for each example in a generated dataset (e.g. class probabilities).

3. Train a new white-box estimator, using generated dataset and generated labels as training data. It means we’re trying to create an estimator which works the same as a black-box estimator, but which is easier to inspect. It doesn’t have to work well globally, but it must approximate the black-box model well in the area close to the original example.

To express “area close to the original example” user must provide a distance/similarity metric for examples in a generated dataset. Then training data is weighted according to a distance from the original example - the further is example, the less it affects weights of a white-box estimator.

4. Explain the original example through weights of this white-box estimator instead.

5. Prediction quality of a white-box classifer shows how well it approximates the black-box classifier. If the quality is low then explanation shouldn’t be trusted.

## eli5.lime¶

To understand how to use eli5.lime with text data check the TextExplainer tutorial. API reference is available here. Currently eli5 doesn’t provide a lot of helpers for LIME + non-text data, but there is an IPyhton notebook with an example of applying LIME for such tasks.

## Caveats¶

It sounds too good to be true, and indeed there are caveats:

1. If a white-box estimator gets a high score on a generated dataset it doesn’t necessarily mean it could be trusted - it could also mean that the generated dataset is too easy and uniform, or that similarity metric provided by user assigns very low values for most examples, so that “area close to the original example” is too small to be interesting.

2. Fake dataset generation is the main issue; it is task-specific to a large extent. So LIME can work with any black-box classifier, but user may need to write code specific for each dataset. There is an opposite tradeoff in inspecting model weights: it works for any task, but one must write inspection code for each estimator type.

eli5.lime provides dataset generation utilities for text data (remove random words) and for arbitrary data (sampling using Kernel Density Estimation).

For text data eli5 also provides eli5.lime.TextExplainer which brings together all LIME steps and allows to explain text classifiers; it still needs to make assumptions about the classifier in order to generate efficient fake dataset.

3. Similarity metric has a huge effect on a result. By choosing neighbourhood of a different size one can get opposite explanations.

## Alternative implementations¶

There is a LIME implementation by LIME authors: https://github.com/marcotcr/lime, so it is eli5.lime which should be considered as alternative. At the time of writing eli5.lime has some differences from the canonical LIME implementation:

1. eli5 supports many white-box classifiers from several libraries, you can use any of them with LIME;
2. eli5 supports dataset generation using Kernel Density Estimation, to ensure that generated dataset looks similar to the original dataset;
3. for explaining predictions of probabilistic classifiers eli5 uses another classifier by default, trained using cross-entropy loss, while canonical library fits regression model on probability output.

There are also features which are supported by original implementation, but not by eli5, and the UIs are different.