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/A3.md from adarshcod30/margadrishti-models: direct link, hf CLI and curl.
- Browser
- Download file 1.73 kB
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https://huggingface.co/adarshcod30/margadrishti-models/resolve/main/model_cards/A3.md
- Command line
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hf download hf://adarshcod30/margadrishti-models/model_cards/A3.md
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curl -L -o A3.md https://huggingface.co/adarshcod30/margadrishti-models/resolve/main/model_cards/A3.md
1.73 kB
| # Model Card · A3 — Will this field capture be rejected at officer review? | |
| *Generated 2026-09-02 from `reports/` — do not edit by hand.* | |
| ## What this model answers | |
| Will this field capture be rejected at officer review? | |
| ## Caveats | |
| - NOT YET ANSWERABLE (F11). The review process changed regime mid-window: February 2024 rejection jumped to 0.790 from ~0.28 while reviewed volume collapsed sixfold. | |
| - Every family is miscalibrated as a result - Brier scores are worse than the prior baseline. This is a property of the label, not the models. | |
| ## 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 | | |
| |---|---|---:|---:|---:|---:|---:|---| | |
| | `xgboost` | gbdt | 0.3088 | 0.5750 | 0.7122 | 0.3962 | 2s | yes | | |
| | `stacked` | ensemble | 0.3084 | 0.5796 | 0.4401 | 0.4023 | 48s | yes | | |
| | `catboost` | gbdt | 0.2987 | 0.5630 | 0.6817 | 0.4033 | 4s | yes | | |
| | `hist_gbm` | gbdt | 0.2955 | 0.5575 | 0.7166 | 0.3960 | 4s | yes | | |
| | `random_forest` | trees | 0.2919 | 0.5683 | 0.5619 | 0.4040 | 2s | yes | | |
| | `logistic` | linear | 0.2893 | 0.5642 | 0.7211 | 0.4068 | 0s | yes | | |
| | `lightgbm` | gbdt | 0.2838 | 0.5522 | 0.7250 | 0.3954 | 2s | yes | | |
| | `extra_trees` | trees | 0.2825 | 0.5544 | 0.4573 | 0.4027 | 1s | yes | | |
| | `prior` | baseline | 0.2459 | 0.5000 | 0.1881 | 0.3947 | 0s | *baseline* | | |
| **Best learned family: `xgboost`** at 0.3088, +25.6% against the strongest baseline (`prior` at 0.2459). | |
| ## Reproduction | |
| ```bash | |
| make data && make ingest && make features | |
| make train | |
| ``` | |
| Seed `42`. Splits are chronological, never random. | |