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Generate Diverse Counterfactual Explanations for any machine learning model.
| Date | Stars |
|---|---|
| 2026-07-31 | 1521 |
| 2026-08-06 | 1522 |
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|PyPiVersion|_ |CondaVersion|_ |MITlicense| |PythonSupport|_ |Downloads|_ |BuildStatusTests|_ |BuildStatusNotebooks|_ .. |MITlicense| image:: https://img.shields.io/badge/License-MIT-blue.svg .. _MITlicense: https://img.shields.io/badge/License-MIT-blue.svg .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dice-ml .. _PyPiVersion: https://pypi.org/project/dice-ml/ .. |Downloads| image:: https://static.pepy.tech/personalized-badge/dice-ml?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads .. _Downloads: https://pepy.tech/project/dice-ml .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dice-ml .. _PythonSupport: https://pypi.org/project/dice-ml/ .. |CondaVersion| image:: https://anaconda.org/conda-forge/dice-ml/badges/version.svg .. _CondaVersion: https://anaconda.org/conda-forge/dice-ml .. |BuildStatusTests| image:: https://github.com/interpretml/DiCE/actions/workflows/python-package.yml/badge.svg?branch=main .. _BuildStatusTests: https://github.com/interpretml/DiCE/actions/workflows/python-package.yml?query=workflow%3A%22Python+package%22 .. |BuildStatusNotebooks| image:: https://github.com/interpretml/DiCE/actions/workflows/notebook-tests.yml/badge.svg?branch=main .. _BuildStatusNotebooks: https://github.com/interpretml/DiCE/actions/workflows/notebook-tests.yml?query=workflow%3A%22Notebook+tests%22 Diverse Counterfactual Explanations (DiCE) for ML ====================================================================== *How to explain a machine learning model such that the explanation is truthful to the model and yet interpretable to people?* `Ramaravind K. Mothilal <https://raam93.github.io/>`_, `Amit Sharma <http://www.amitsharma.in/>`_, `Chenhao Tan <https://chenhaot.com/>`_ `FAT* '20 paper <https://arxiv.org/abs/1905.07697>`_ | `Docs <https://interpretml.github.io/DiCE/>`_ | `Example Notebooks <https://github.com/interpretml/DiCE/tree/master/docs/source/notebooks>`_ | Live Jupyter notebook |Binder|_ .. |Binder| image:: https://mybinder.org/badge_logo.svg .. _Binder: https://mybinder.org/v2/gh/interpretML/DiCE/master?filepath=docs/source/notebooks **Blog Post**: `Explanation for ML using diverse counterfactuals <https://www.microsoft.com/en-us/research/blog/open-source-library-provides-explanation-for-machine-learning-through-diverse-counterfactuals/>`_ **Case Studies**: `Towards Data Science <https://towardsdatascience.com/dice-diverse-counterfactual-explanations-for-hotel-cancellations-762c311b2c64>`_ (Hotel Bookings) | `Analytics Vidhya <https://medium.com/analytics-vidhya/dice-ml-models-with-counterfactual-explanations-for-the-sunk-titanic-30aa035056e0>`_ (Titanic Dataset) .. image:: https://www.microsoft.com/en-us/research/uploads/prod/2020/01/MSR-Amit_1400x788-v3-1blog.gif :align: center :alt: Visualizing a counterfactual explanation Explanations are critical for machine learning, especially as machine learning-based systems are being used to inform decisions in societally critical domains such as finance, healthcare, education, and criminal justice. However, most explanation methods depend on an approximation of the ML model to create an interpretable explanation. For example, consider a person who applied for a loan and was rejected by the loan distribution algorithm of a financial company. Typically, the company may provide an explanation on why the loan was rejected, for example, due to "poor credit history". However, such an explanation does not help the person decide *what they should do next* to improve their chances of being approved in the future. Critically, the most important feature may not be enough to flip the decision of the algorithm, and in practice, may not even be changeable such as gender and race. DiCE implements `counterfactual (CF) explanations <https://arxiv.org/abs/1711.00399>`_ that provide this information by showing feature-perturbed versions
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4551dff6e1e124b9, llm:Repository topics and description: topics include counterfactual-explanations, explainable-ai, explainable-ml, interpretable-machine-learning; description: 'Generate Diverse Counterfactual Explanations for any machine learning model.' Readme references DiCE (Diverse Counterfactual Explanations) for ML.
matched fp:4551dff6e1e124b9, llm:Repository topics and description: topics include counterfactual-explanations, explainable-ai, explainable-ml, interpretable-machine-learning; description: 'Generate Diverse Counterfactual Explanations for any machine learning model.' Readme references DiCE (Diverse Counterfactual Explanations) for ML.