Published April 29, 2020
| Version v0.11.1
Software
Open
alan-turing-institute/MLJ.jl: v0.11.1
Creators
- Anthony Blaom, PhD1
- Thibaut Lienart2
- Yiannis Simillides
- Diego Arenas
- vollmersj
- Mosè Giordano3
- Okon Samuel
- Ayush Shridhar4
- Ayush Shridhar
- Ed
- swenkel
- Julian Samaroo
- evalparse5
- Júlio Hoffimann6
- sjvollmer
- Michael Krabbe Borregaard7
- Kevin Squire8
- pshashk
- lhnguyen-vn
- azev779
- Ashrya Agrawal10
- Venkateshprasad
- Robert Hönig11
- Nils12
- Kryohi
- Julia TagBot
- Evelina Gabasova13
- Dilum Aluthge14
- Cédric St-Jean15
- 1. NeSI/Alan Turing Institute
- 2. AWS
- 3. @UCL-RITS
- 4. IIIT Bhubaneswar
- 5. @evalparse
- 6. IBM Research
- 7. GLOBE Institute
- 8. SecondSpectrum
- 9. Zicklin School of Business, Baruch College
- 10. @bitsacm
- 11. University of Cambridge
- 12. Queen Mary, University of London
- 13. The Alan Turing Institute
- 14. Brown University
- 15. r2.ca
Description
MLJ v0.11.1
Minor issues only:
- [x] #497
- [x] Revise cheatsheet (#474)
Closed issues:
- Add sample-weight interface point? (#177)
- Add default_measure to learning_curve! (#283)
- Flush out unsupervised models in "Adding models for general use" section of manual (#285)
- is_probabilistic=true in @pipeline syntax is clunky (#305)
- [suggestions] Unroll the network in
@from_network(#311) - Towards stabilisation of the core API (#318)
- failure on nightly (1.4) (#384)
- Documentation of extracting best fitted params (#386)
- incorporate input_scitype and target_scitype declarations for @pipeline models (#412)
- "Supervised" models with no predict method (#460)
- Use OpenML.load to iris data set in the Getting Started page of docs? (#461)
- Review cheatsheet (#474)
- Re-export UnsupervisedNetwork from MLJBase (#497)
- Broken link for MLJ tour in documentation (#501)
Merged pull requests:
- For a 0.11.1 release (#506) (@ablaom)
Files
alan-turing-institute/MLJ.jl-v0.11.1.zip
Files
(3.7 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/alan-turing-institute/MLJ.jl/tree/v0.11.1 (URL)