CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox

Artelt A (2019)
Bielefeld University.

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Abstract / Bemerkung
**CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox**
ceml is a Python toolbox for computing counterfactuals. Counterfactuals can be used to explain the predictions of machine learning models. It supports many common machine learning frameworks: - scikit-learn - PyTorch - Keras - Tensorflow Furthermore, ceml is easy to use and can be extended very easily. See the documentation and user guide for more information on how to use and extend ceml.
Stichworte
python; machine learning; counterfactual explanations
Erscheinungsjahr
2019
Page URI
https://pub.uni-bielefeld.de/record/2936468

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Artelt A. CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox . Bielefeld University; 2019.
Artelt, A. (2019). CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox . Bielefeld University. https://doi.org/10.4119/unibi/2936468
Artelt, André. 2019. CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox . Bielefeld University.
Artelt, A. (2019). CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox . Bielefeld University.
Artelt, A., 2019. CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox , Bielefeld University.
A. Artelt, CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox , Bielefeld University, 2019.
Artelt, A.: CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox . Bielefeld University (2019).
Artelt, André. CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox . Bielefeld University, 2019.
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2019-09-25T06:54:10Z
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