InseeFrLab / InseeFrLab/benchmark_spatial_interpolation
Revisions of the report
- Lingua principale
- Jupyter Notebook
- Stelle
- 1
- Fork
- 1
- Metriche di merge delle PR
- Nessuna PR unita negli ultimi 30g
Descrizione
- I think you can shorten the section on coordinate rotation to half a page, maybe a bit more. Do not mention 23 rotations, only $k$ rotations.
- You can mix the section on data and subsection 4.5 (Experimental Setup) in only one section (after the theoretical part).
- The table ` Benchmark Configuration` can be replaced by one paragraph.
- In 5.1, you can hide the code producing the heatmap (it's a quarto chunk option).
Results:
- Replace the figures `Average Training Time on Small Datasets (All Types)` and `Efficiency Landscape: Initial Speed vs. Scalability Cost` by a table, with NA for GAM and GeoRF on large data. The scalability can be discussed in the text, by comparing the training time on large and small data.
- Replace the figure `Predictive Scalability: R2 Improvement (Small Large Data)` by a table, and replace R² by the MSE.
- I'm not sure that the figure `Impact of Grid Structure on Model Performance (R2)` is useful. Maybe a comment in the text would be sufficient?
Guida per i contributori
Nessuna guida per i contributori indicizzata per questo repository
Direzione di ricerca
Start with the report sections named in the issue, especially coordinate rotation, Experimental Setup, section 5.1, and the listed results figures. Done means the requested prose, tables, metrics, and hidden heatmap code are updated, with the questionable grid-structure figure either removed or replaced by a text comment.
Scritto dal modello di indicizzazione a partire dal testo della issue.
Valutazione
- Stack tecnologico
- jupyter-notebook
- Ambito
- documentation
- Tipo di issue
- Documentazione
- Difficoltà
- 4/5
- Tempo stimato
- 3-5 giorni
- Stato di attività
- Ferma
- Chiarezza
- Abbastanza chiara
- Idoneità per principianti
- 35/100