InseeFrLab / InseeFrLab/benchmark_spatial_interpolation

Revisions of the report

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Beschreibung

- 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?

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Rechercherichtung

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.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Bewertung

Tech-Stack
jupyter-notebook
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documentation
Issue-Typ
Dokumentation
Schwierigkeit
4/5
Geschätzter Aufwand
3-5 Tage
Aktivitätsstatus
Veraltet
Klarheit
Größtenteils klar
Anfängerfreundlichkeit
35/100

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