Comments (6)
ping @nicholasloveday I'm happy with this page as-is, but it would be fine to make some of the suggestions Steph has made also. Just let us know what you think.
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I have since had some additional thoughts.
I think it might be worth mentioning the following two things (I haven't work-shopped the language, I just typed these out quickly):
- Can be used at any scale - from megabytes to petabytes
- Something about preserving and reducing dimensions
(Obviously the language would need fixing)
Also, is it additionally worth mentioning that scores
includes consistent and threshold weighted scores?
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Regarding questions 2 and 4, I suggest changing the "complex scores..." line to
"recently developed, user-focused scores and diagnostics including threshold weighted scores (e.g., threshold weighted continuous ranked probability score (twCRPS) (Gneiting and Ranjan 2011) and threshold weighted mean squared error (MSE) (Taggart 2022), Murphy diagrams (Ehm et al., 2016), and isotonic regression for reliability diagrams (Dimitriadis et al., 2021).
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Regarding question 3. I don't feel like any changes need to be made
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Can be used at any scale - from megabytes to petabytes
This relates to the existing line "Uses Dask for scaling and performance.", which I think is fine
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Thanks for that helpful feedback @nicholasloveday. I'll update the page to incorporate it.
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Related Issues (20)
- Tutorial Gallery - put headers in own cells so they render better in readthedocs
- [JOSS] Installation of jupyer kernel HOT 2
- [JOSS] Instructions for downloading example data HOT 1
- [JOSS] General explanation of reduce/preserve HOT 3
- [JOSS] Minimal pandas support HOT 4
- [JOSS] Implementation of weights is occasionally unclear HOT 1
- rename correlation HOT 1
- Badges, CI and forks HOT 2
- roc_curve_data API rendering in readthedocs HOT 1
- Add threshold weighted scores
- Go through and add thumbnails to each tutorial gallery entry
- Kling-Gupta efficiency and percentage bias HOT 10
- Add link to JOSS article towards start of README HOT 5
- Brier Score - References in API & Tutorial
- Relative Economic value
- Badges for PyPI and Conda
- twcrps for ensembles
- Update Maintainers Notes
- Mathjax: x and y vs forecast and observed
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