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
File size: 1,719 Bytes
55151a3 dc392dc 55151a3 dc392dc 55151a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # Model Card · A2 — Is this cell-hour in the top decile of violation intensity?
*Generated 2026-09-02 from `reports/` — do not edit by hand.*
## What this model answers
Is this cell-hour in the top decile of violation intensity?
## Caveats
- The original goal of PR-AUC >= 0.45 was unreachable by construction (F10): it assumed ~10% prevalence, but only 2.94% of cell-hours are non-zero, so the label lands at 0.291%.
- Read scores against the 0.291% base rate, not against 0.45. The best learned family is a 46.9x 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.
| Model | Family | PR-AUC ↑ | ROC-AUC ↑ | Brier ↓ | Best F1 ↑ | Fit | Beats baseline |
|---|---|---:|---:|---:|---:|---:|---|
| `random_forest` | trees | 0.1365 | 0.9380 | 0.0027 | 0.2312 | 64s | yes |
| `catboost` | gbdt | 0.1305 | 0.9341 | 0.0027 | 0.2203 | 35s | yes |
| `xgboost` | gbdt | 0.1281 | 0.9368 | 0.0027 | 0.2153 | 20s | yes |
| `extra_trees` | trees | 0.1218 | 0.9362 | 0.0027 | 0.2138 | 23s | yes |
| `stacked` | ensemble | 0.1200 | 0.9363 | 0.0028 | 0.1902 | 233s | yes |
| `logistic` | linear | 0.1127 | 0.9204 | 0.0027 | 0.2074 | 11s | yes |
| `hist_gbm` | gbdt | 0.0881 | 0.9128 | 0.0031 | 0.1896 | 4s | yes |
| `lightgbm` | gbdt | 0.0357 | 0.9229 | 0.0054 | 0.0926 | 14s | yes |
| `prior` | baseline | 0.0029 | 0.5000 | 0.0029 | 0.0058 | 0s | *baseline* |
**Best learned family: `random_forest`** at 0.1365, 46.9x the strongest baseline (`prior` at 0.0029).
## Reproduction
```bash
make data && make ingest && make features
make train
```
Seed `42`. Splits are chronological, never random.
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