<commit_before># lime

[![Build Status](https://travis-ci.org/marcotcr/lime.svg?branch=master)](https://travis-ci.org/marcotcr/lime)

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:<commit_msg>Add binder link to README<commit_after># lime

[![Build Status](https://travis-ci.org/marcotcr/lime.svg?branch=master)](https://travis-ci.org/marcotcr/lime)
[![Binder](https://mybinder.org/badge_logo.svg)](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:
{'fraction_non_alphanumeric': 0.10212201591511937, 'fraction_numerical': 0.008289124668435014, 'mean_word_length': 5.070422535211268, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 16, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3241722', 'n_tokens_mistral': 924, 'n_tokens_neox': 863, 'n_words': 336}