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Lime: Explaining the predictions of any machine learning classifier
| Date | Stars |
|---|---|
| 2026-07-31 | 12161 |
| 2026-08-01 | 12161 |
| 2026-08-06 | 12166 |
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# lime [](https://travis-ci.org/marcotcr/lime) [](https://mybinder.org/v2/gh/marcotcr/lime/master) This project is about explaining what machine learning classifiers (or models) are doing. 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). Lime is based on the work presented in [this paper](https://arxiv.org/abs/1602.04938) ([bibtex here for citation](https://github.com/marcotcr/lime/blob/master/citation.bib)). Here is a link to the promo video: <a href="https://www.youtube.com/watch?v=hUnRCxnydCc" target="_blank"><img src="https://raw.githubusercontent.com/marcotcr/lime/master/doc/images/video_screenshot.png" width="450" alt="KDD promo video"/></a> Our plan is to add more packages that help users understand and interact meaningfully with machine learning. Lime is able to explain any black box classifier, with two or more classes. All we require is that the classifier implements a function that takes in raw text or a numpy array and outputs a probability for each class. Support for scikit-learn classifiers is built-in. ## Installation The lime package is on [PyPI](https://pypi.python.org/pypi/lime). Simply run: ```sh pip install lime ``` Or clone the repository and run: ```sh pip install . ``` We dropped python2 support in `0.2.0`, `0.1.1.37` was the last version before that. ## Screenshots Below are some screenshots of lime explanations. These are generated in html, and can be easily produced and embedded in ipython notebooks. We also support visualizations using matplotlib, although they don't look as nice as these ones. #### Two class case, text Negative (blue) words indicate atheism, while positive (orange) words indicate christian. The way to interpret the weights by applying them to the prediction probabilities. For example, if we remove the words Host and NNTP from the document, we expect the classifier to predict atheism with probability 0.58 - 0.14 - 0.11 = 0.31.  #### Multiclass case  #### Tabular data  #### Images (explaining prediction of 'Cat' in pros and cons) <img src="https://raw.githubusercontent.com/marcotcr/lime/master/doc/images/images.png" width=200 /> ## Tutorials and API For example usage for text classifiers, take a look at the following two tutorials (generated from ipython notebooks): - [Basic usage, two class. We explain random forest classifiers.](https://marcotcr.github.io/lime/tutorials/Lime%20-%20basic%20usage%2C%20two%20class%20case.html) - [Multiclass case](https://marcotcr.github.io/lime/tutorials/Lime%20-%20multiclass.html) For classifiers that use numerical or categorical data, take a look at the following tutorial (this is newer, so please let me know if you find something wrong): - [Tabular data](https://marcotcr.github.io/lime/tutorials/Tutorial%20-%20continuous%20and%20categorical%20features.html) - [Tabular data with H2O models](https://marcotcr.github.io/lime/tutorials/Tutorial_H2O_continuous_and_cat.html) - [Latin Hypercube Sampling](doc/notebooks/Latin%20Hypercube%20Sampling.ipynb) For image classifiers: - [Images - basic](https://marcotcr.github.io/lime/tutorials/Tutorial%20-%20images.html) - [Images - Faces](https://github.com/marcotcr/lime/blob/master/doc/notebooks/Tutorial%20-%20Faces%20and%20GradBoost.ipynb) - [Images with Keras](https://github.com/marcotcr/lime/blob/master/doc/notebooks/Tutorial%20-%20Image%20Classification%20Keras.ipynb) - [MNIST with random forests](https://github.com/marcotcr/lime/blob/master/doc/notebooks/Tutorial%20-%20MNIST%20and%20RF.ipynb) - [Images with PyTorch](https://github.com/marcotcr/lime/
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Marco Tulio Correia Ribeiro
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Nicole White
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James Myatt · United Kingdom
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Christoph Molnar · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5d88fbd9f0b6e374, llm:Description: 'Lime: Explaining the predictions of any machine learning classifier' (repo marcotcr/lime).
matched fp:5d88fbd9f0b6e374, llm:Description: 'Lime: Explaining the predictions of any machine learning classifier' (repo marcotcr/lime).