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
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Download model_cards/B2.md from adarshcod30/margadrishti-models: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
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https://huggingface.co/adarshcod30/margadrishti-models/resolve/main/model_cards/B2.md
- Command line
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hf download hf://adarshcod30/margadrishti-models/model_cards/B2.md
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curl -L -o B2.md https://huggingface.co/adarshcod30/margadrishti-models/resolve/main/model_cards/B2.md
1.57 kB
| # 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 | |
| ```bash | |
| make data && make ingest && make features | |
| make train-b | |
| ``` | |
| Seed `42`. Splits are chronological, never random. | |