Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making

arXiv:2403.12664v1 Announce Type: new
Abstract: In many applications, model ensembling proves to be better than a single predictive model. Hence, it is the most common post-processing technique in Automated Machine Learning (AutoML). The most popular frameworks use ensembles at the expense of reducing the interpretability of the final models. In our work, we propose cattleia - an application that deciphers the ensembles for regression, multiclass, and binary classification tasks. This tool works with models built by three AutoML packages: auto-sklearn, …

abstract applications arxiv automated automated machine learning automl cs.ai cs.lg decision frameworks interpretability machine machine learning making popular post-processing predictive processing type work