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

arXiv:2404.09053v1 Announce Type: new
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 …

arxiv cs.hc cs.lg feature feature selection insights machine machine learning machine learning insights stat.ap stat.ml type