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/B1.md from adarshcod30/margadrishti-models: direct link, hf CLI and curl.
- Browser
- Download file 2.33 kB
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https://huggingface.co/adarshcod30/margadrishti-models/resolve/main/model_cards/B1.md
- Command line
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hf download hf://adarshcod30/margadrishti-models/model_cards/B1.md
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curl -L -o B1.md https://huggingface.co/adarshcod30/margadrishti-models/resolve/main/model_cards/B1.md
2.33 kB
Model Card · B1 — How long until this event is cleared?
Generated 2026-09-02 from reports/ — do not edit by hand.
What this model answers
How long until this event is cleared?
Caveats
- Predicts ADMINISTRATIVE closure (ticket closed), not physical road-clear time.
- n=2,720 of 8,173 events carry a usable duration (F4). Metrics are means over five chronological folds with a standard deviation, because a single split at this size is one draw from a small sample.
Results
Ranked by Deviance ↓ (lower 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 | Deviance ↓ | MAE min ↓ | RMSE ↓ | R² ↑ | Fit | Beats baseline |
|---|---|---|---|---|---|---|---|
xgboost |
gbdt | 0.4990 ± 0.128 | 538.6806 | 1.4936 | 0.2428 | 1s | yes |
random_forest |
trees | 0.5034 ± 0.134 | 546.6247 | 1.5039 | 0.2350 | 0s | yes |
extra_trees |
trees | 0.5084 ± 0.122 | 551.1427 | 1.5178 | 0.2170 | 0s | yes |
lightgbm |
gbdt | 0.5119 ± 0.133 | 549.3335 | 1.5196 | 0.2177 | 1s | yes |
hist_gbm |
gbdt | 0.5322 ± 0.120 | 549.0341 | 1.5411 | 0.1887 | 4s | yes |
hist_gbm_poisson |
gbdt | 0.5363 ± 0.152 | 552.7476 | 1.5548 | 0.1832 | 1s | yes |
ridge |
linear | 0.5401 ± 0.151 | 554.2421 | 1.5587 | 0.1781 | 0s | yes |
poisson_glm |
linear | 0.5445 ± 0.160 | 555.4239 | 1.5670 | 0.1703 | 0s | yes |
catboost |
gbdt | 0.5468 ± 0.112 | 538.7609 | 1.5571 | 0.1721 | 2s | yes |
mean |
baseline | 0.6904 ± 0.213 | 554.9495 | 1.7510 | -0.0338 | 0s | baseline |
torch_mlp |
deep | 4.4045 ± 3.908 | 14098.0595 | 10.6682 | -54.3918 | 1s | no |
Best learned family: xgboost at 0.4990, +27.7% against the strongest baseline (mean at 0.6904).
Scored on their own metric, not on poisson_deviance
These ran and are reported; the target's primary metric simply does not apply to them.
| Model | Family | Metric | Value |
|---|---|---|---|
lightgbm_q50 |
quantile | Pinball ↓ | 0.5658 ± 0.090 |
lightgbm_q90 |
quantile | Pinball ↓ | 0.2343 ± 0.040 |
Reproduction
make data && make ingest && make features
make train-b
Seed 42. Splits are chronological, never random.