Tabular Classification
Scikit-learn
Joblib
English
French
wildfire
fire-weather-index
scikit-learn
logistic-regression
france
var
open-data
Instructions to use GuillaumeGay/firepredict with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use GuillaumeGay/firepredict with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("GuillaumeGay/firepredict", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download metadata.json from GuillaumeGay/firepredict: direct link, hf CLI and curl.
- Browser
- Download file 5.87 kB
-
https://huggingface.co/GuillaumeGay/firepredict/resolve/main/metadata.json
- Command line
-
hf download hf://GuillaumeGay/firepredict/metadata.json
-
curl -L -o metadata.json https://huggingface.co/GuillaumeGay/firepredict/resolve/main/metadata.json
5.87 kB
| { | |
| "version": "small_commune_v2", | |
| "frozen_at": "2026-11-09T12:35:04+00:00", | |
| "git_commit": "5fa2bc109a9eccab69a1f26349baec04b89ad6d9", | |
| "question": "chance that at least one fire >= 0.1 ha starts in the commune on the target day; issued the morning before, from data up to two days before the target day", | |
| "inputs": [ | |
| "log_usual_rate", | |
| "log_local_fwi", | |
| "local_wb_30d", | |
| "orange_period", | |
| "red_period" | |
| ], | |
| "weights_standardised": { | |
| "log_usual_rate": 0.5893, | |
| "log_local_fwi": 0.8582, | |
| "local_wb_30d": -0.5619, | |
| "orange_period": 0.2303, | |
| "red_period": -0.2605 | |
| }, | |
| "fill_values": { | |
| "log_usual_rate": -1.460061, | |
| "log_local_fwi": 2.703165, | |
| "local_wb_30d": -34.599998, | |
| "orange_period": 0.0, | |
| "red_period": 0.0 | |
| }, | |
| "training_years": [ | |
| 2006, | |
| 2007, | |
| 2008, | |
| 2009, | |
| 2010, | |
| 2011, | |
| 2012, | |
| 2013, | |
| 2014, | |
| 2015, | |
| 2016, | |
| 2017, | |
| 2018, | |
| 2019, | |
| 2020, | |
| 2021, | |
| 2022, | |
| 2023, | |
| 2025 | |
| ], | |
| "training_rows": 1052640, | |
| "training_positive_commune_days": 902, | |
| "bdtopo_edition": "BDTOPO_3-5_TOUSTHEMES_GPKG_LAMB93_D083_2026-09-15", | |
| "raw_data": { | |
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