MLXP: A framework for conducting replicable Machine Learning eXperiments in Python

arXiv:2402.13831v1 Announce Type: new
Abstract: Replicability in machine learning (ML) research is increasingly concerning due to the utilization of complex non-deterministic algorithms and the dependence on numerous hyper-parameter choices, such as model architecture and training datasets. Ensuring reproducible and replicable results is crucial for advancing the field, yet often requires significant technical effort to conduct systematic and well-organized experiments that yield robust conclusions. Several tools have been developed to facilitate experiment management and enhance reproducibility; however, they often introduce complexity …

arxiv cs.lg cs.se framework machine machine learning machine learning experiments python type