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Author:
Roman Lutz, Ilya Matiach, Ke Xu
Summary:
Interactive visualizations to assess fairness, explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
Latest version:
0.0.2
Required dependencies:
al360-erroranalysis
|
al360-tai-core-flask
|
al360-taiutils
|
al360-trustworthyai
|
fairlearn
|
lightgbm
|
numpy
|
pandas
|
scikit-learn
|
scipy
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