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: 1,260 Bytes
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 | {
"highest_concern": "high",
"findings": [
{
"name": "feedback loop",
"statistic": 0.6133053955548047,
"concern": "moderate",
"interpretation": "Spearman rho=+0.613 between score and historical patrol hours - recommendations substantially track past patrolling"
},
{
"name": "under-observation coverage",
"statistic": 0.0,
"concern": "high",
"interpretation": "no recommendation reaches an under-observed cell, though 25.8% of cells are flagged - the system only looks where someone already looked"
},
{
"name": "station disparity",
"statistic": 0.20912368007966697,
"concern": "high",
"interpretation": "recommendation Gini 0.796 vs violation Gini 0.587 (excess +0.209) across 53 stations"
},
{
"name": "counterfactual patrol",
"statistic": 0.3,
"concern": "moderate",
"interpretation": "only 30% of the top-50 cells survive equalising patrol effort (full-ranking tau +0.318)"
}
],
"coverage": {
"cells_assigned": 8,
"cells_total": 1802,
"risk_covered": 5.038800746202469,
"risk_total": 48.28950881958008,
"risk_covered_share": 0.10434566160174677,
"under_observed_assigned": 0,
"seed": 42
}
} |