ALICE: Combining Feature Selection and Inter-Rater Agreeability for Machine Learning Insights

April 13, 2024 · Entered Twilight · 🏛 arXiv.org

Repo contents: .gitattributes, .gitignore, README.md, Telco_customer_churn.xlsx, alice, alice_framework_graph.png, class_telco.pkl, clean_data, customer_churn_dataprocessing.ipynb, customer_churn_test.ipynb, experiment_results.png, pyproject.toml, reg_telco.pkl, requirements.txt, results_analysis.ipynb, test_results

Authors Bachana Anasashvili, Vahidin Jeleskovic arXiv ID 2404.09053 Category cs.LG: Machine Learning Cross-listed cs.HC, stat.AP, stat.ML Citations 0 Venue arXiv.org Repository https://github.com/anasashb/aliceHU ⭐ 3 Last Checked 3 months ago

Abstract

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