Instructions to use adarshcod30/margadrishti-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use adarshcod30/margadrishti-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("adarshcod30/margadrishti-models", "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
Model Card · B2 — Will this event require a road closure?
Generated 2026-09-02 from reports/ — do not edit by hand.
What this model answers
Will this event require a road closure?
Caveats
- 8.727% positive class. Read PR-AUC against that prevalence, not against 1.0 - the best family is a 4.1x lift over it.
Results
Ranked by PR-AUC ↑ (higher is better). Every family that ran is listed, including those that lost — the comparison is the deliverable. Figures are means over 5 chronological folds, ± one standard deviation.
| Model | Family | PR-AUC ↑ | ROC-AUC ↑ | Brier ↓ | Best F1 ↑ | Fit | Beats baseline |
|---|---|---|---|---|---|---|---|
catboost |
gbdt | 0.3615 ± 0.071 | 0.7684 | 0.0688 | 0.4323 | 3s | yes |
random_forest |
trees | 0.3578 ± 0.080 | 0.7723 | 0.0712 | 0.4326 | 1s | yes |
lightgbm |
gbdt | 0.3471 ± 0.060 | 0.7583 | 0.0746 | 0.4144 | 6s | yes |
xgboost |
gbdt | 0.3445 ± 0.081 | 0.7565 | 0.0703 | 0.4093 | 1s | yes |
logistic |
linear | 0.3304 ± 0.065 | 0.7584 | 0.0713 | 0.3818 | 0s | yes |
hist_gbm |
gbdt | 0.3269 ± 0.061 | 0.7366 | 0.0724 | 0.3855 | 7s | yes |
extra_trees |
trees | 0.3024 ± 0.083 | 0.7443 | 0.0734 | 0.3883 | 0s | yes |
prior |
baseline | 0.0873 ± 0.015 | 0.5000 | 0.0798 | 0.1602 | 0s | baseline |
Best learned family: catboost at 0.3615, 4.1x the strongest baseline (prior at 0.0873).
Reproduction
make data && make ingest && make features
make train-b
Seed 42. Splits are chronological, never random.