1) yellowbrick
2) ELI5
3) lime
4) mlxtend
As concerns regarding bias in artificial intelligence become more prominent it is becoming more and more important for businesses to be able to explain both the predictions their models are producing and how the models themselves work.
Fortunately, there is an increasing number of python libraries being developed that attempt to solve this problem. In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.
These libraries are all pip installable, come with good documentation and have an emphasis on visual interpretation.
Credits: https://towardsdatascience.com/python-libraries-for-interpretable-machine-learning-c476a08ed2c7
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