Work program

- Tools to learn:
- `git`, `uv`, `scikit-learn`, s3, MLFlow, Argo, `quarto`.
- scikit-learn: Pipeline, transformer, estimator, CV, transform/fit/predict.
- How to define and use a configuration on SSP Cloud.
- Regarding machine learning algorithms:
- Read the introduction to [ensemble methods](https://inseefrlab.github.io/DT_methodes_ensemblistes/);
- Find and read papers on spatial interpolation using tree-based methods, and produce a literature review.
- Establish the list of algorithms to compare.
- Define the list of metrics to compare.
- Build the datasets:
- Define what sort of data we want to work on.
- Gather real datasets;
- Generate artificial datasets (using `gstools`?);
- Build code that automatically trains a suite of algorithms;
- Train basic models;
- Tune hyperparameters;
- Log models using MLFlow;
- Orchestrate training using Argo (optional).
- Prepare an automated report using Quarto.

Outputs:

- A fully reproducible evaluation pipeline in a clean Github repository.
- A report describing the whole project.
- A slideshow presenting the results.
- A series of clean and well-documented datasets usable for other benchmarks.