ALICE: Combining Feature Selection and Inter-Rater Agreeability for
  Machine Learning Insights
Bachana Anasashvili Vahidin Jeleskovic
This paper presents a new Python library called Automated Learning for
Insightful Comparison and Evaluation (ALICE), which merges conventional feature
selection and the concept of inter-rater agreeability in a simple,
user-friendly manner to seek insights into black box Machine Learning models.
The framework is proposed following an overview of the key concepts of
interpretability in ML. The entire architecture and intuition of the main
methods of the framework are also thoroughly discussed and results from initial
experiments on a customer churn predictive modeling task are presented,
alongside ideas for possible avenues to explore for the future. The full source
code for the framework and the experiment notebooks can be found at:
https://github.com/anasashb/aliceHU