OpenML: networked science in machine learning
Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.
Code (1)
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
OpenML-Python: an extensible Python API for OpenML
OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper we introduce OpenML-Python, a client API for Python, open…
BIG-bench Machine LearningOpen science in machine learning
We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application. They go beyond the more traditional repositories f…
BIG-bench Machine LearningOpenML: An R Package to Connect to the Machine Learning Platform OpenML
OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we pr…
BIG-bench Machine LearningOpenML Benchmarking Suites
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, …
BenchmarkingBIG-bench Machine LearningGeneral ClassificationFrontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introd…