https://github.com/ccs-lab/easyml
A toolkit for easily building and evaluating machine learning models.
Science Score: 33.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 8 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org, zenodo.org -
✓Committers with academic emails
1 of 5 committers (20.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.0%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
A toolkit for easily building and evaluating machine learning models.
Basic Info
- Host: GitHub
- Owner: CCS-Lab
- License: other
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://ccs-lab.github.io/easyml
- Size: 25.7 MB
Statistics
- Stars: 40
- Watchers: 8
- Forks: 16
- Open Issues: 9
- Releases: 2
Topics
Metadata Files
README.md
easyml
A toolkit for easily building and evaluating machine learning models.
Installation
See installation instructions for the Python or R packages.
If you encounter a clear bug, please file a minimal reproducible example on github.
Citation
A whitepaper for easyml is available at https://doi.org/10.1101/137240. If you find this code useful please cite us in your work:
@article {Hendricks137240,
author = {Hendricks, Paul and Ahn, Woo-Young},
title = {Easyml: Easily Build And Evaluate Machine Learning Models},
year = {2017},
doi = {10.1101/137240},
publisher = {Cold Spring Harbor Labs Journals},
URL = {http://biorxiv.org/content/early/2017/05/12/137240},
journal = {bioRxiv}
}
References
Hendricks, P., & Ahn, W.-Y. (2017). Easyml: Easily Build And Evaluate Machine Learning Models. bioRxiv, 137240. http://doi.org/10.1101/137240
Owner
- Name: Computational Clinical Science Laboratory
- Login: CCS-Lab
- Kind: organization
- Location: Seoul National University (Seoul, Korea)
- Website: ccs-lab.github.io
- Repositories: 16
- Profile: https://github.com/CCS-Lab
GitHub Events
Total
Last Year
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| paulhendricks | p****3@o****u | 507 |
| Woo-Young Ahn | y****n | 27 |
| Nathaniel Haines | h****5@C****l | 3 |
| Young Ahn | w****n@g****m | 3 |
| Yedarm Seong | m****7@g****m | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: almost 2 years ago
All Time
- Total issues: 60
- Total pull requests: 40
- Average time to close issues: 3 months
- Average time to close pull requests: 1 day
- Total issue authors: 7
- Total pull request authors: 5
- Average comments per issue: 1.58
- Average comments per pull request: 0.23
- Merged pull requests: 29
- Bot issues: 0
- Bot pull requests: 9
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- paulhendricks (39)
- youngahn (14)
- Nathaniel-Haines (3)
- ghshin-github (1)
- irisshen926 (1)
- brainconnectome (1)
- iSteal (1)
Pull Request Authors
- paulhendricks (26)
- dependabot[bot] (9)
- Nathaniel-Haines (2)
- youngahn (2)
- mybirth0407 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 8 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 2
- Total maintainers: 1
pypi.org: easymlpy
A Python toolkit for easily building and evaluating machine learning models.
- Homepage: https://github.com/CCS-Lab/easyml
- Documentation: https://easymlpy.readthedocs.io/
- License: MIT
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Latest release: 0.1.2
published about 9 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.3.1 depends
- caret * imports
- corrplot * imports
- dummies * imports
- e1071 * imports
- futile.logger * imports
- ggplot2 * imports
- glinternet * imports
- glmnet * imports
- nnet * imports
- pROC * imports
- parallel * imports
- pbapply * imports
- pbmcapply * imports
- randomForest * imports
- scales * imports
- covr * suggests
- knitr * suggests
- lintr * suggests
- rmarkdown * suggests
- testthat * suggests
- easymlpy ==0.1.2
- progressbar2 ==3.11.0
- requests ==2.12.4
- Babel ==2.3.4
- CommonMark ==0.5.4
- Jinja2 ==2.9.4
- MarkupSafe ==0.23
- PyYAML ==3.12
- Pygments ==2.1.3
- Sphinx ==1.5.1
- alabaster ==0.7.9
- argh ==0.26.2
- awscli ==1.11.54
- botocore ==1.5.17
- colorama ==0.3.7
- cycler ==0.10.0
- docutils ==0.13.1
- easymlpy ==0.1.1
- glmnet ==1.0.0
- imagesize ==0.7.1
- jmespath ==0.9.1
- livereload ==2.5.1
- matplotlib ==1.5.3
- numpy ==1.11.2
- pandas ==0.19.1
- pathtools ==0.1.2
- port-for ==0.3.1
- progressbar2 ==3.11.0
- py ==1.4.32
- pyasn1 ==0.2.2
- pyparsing ==2.1.10
- pytest ==3.0.5
- python-dateutil ==2.6.0
- python-utils ==2.0.0
- pytz ==2016.7
- recommonmark ==0.4.0
- requests ==2.12.4
- rsa ==3.4.2
- s3transfer ==0.1.10
- scikit-learn ==0.18.1
- scikit-plot ==0.2.3
- scipy ==0.18.1
- seaborn ==0.7.1
- six ==1.10.0
- snowballstemmer ==1.2.1
- sphinx-autobuild ==0.6.0
- tornado ==4.4.2
- watchdog ==0.8.3