Skip to content

nodechef/openML_docker_server

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

openML_docker_server

Server for OPENML website

Installation:

  1. Download Docker from https://www.docker.com/what-docker
  2. Run Docker.
  3. Clone this repository and simply run docker-compose up in your terminal in the directory where the repository is cloned.
  4. Visit Localhost:3000

OpenML: Open Machine Learning

Aim

OpenML aims to create a novel ecosystem for machine learning experimentation. The current generation of machine learning and data mining platforms offers a wide variety of algorithms to process and model all kinds of data. They also offer convenient ways for running many experiments to assess, select and fine-tune algorithm performance and optimize workflows. On the whole, however, users only have access to their own experiments: there is no global repository for machine learning experiments, nor a standardized way to share experiments with other users. This means that a lot of valuable knowledge about machine learning techniques is lost. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.

How does it work?

OpenML works much like a data mining challenge platform, such as Kaggle, except that solutions are constructed collaboratively, with anybody free to build on other people's work, instead of a competition setting, where all progress is kept secret.

About

Server for OPENML website

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors