Instructions to use jorgeasmz/fraud-stream-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use jorgeasmz/fraud-stream-detector with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("jorgeasmz/fraud-stream-detector", "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: 1,185 Bytes
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"threshold": 0.01643005512716834,
"daily_budget": 100,
"training_days": 91,
"training_rows": 872795,
"held_out_rows": 813843,
"held_out_days": 85,
"alerts_per_day_at_threshold": 120.1,
"precision_at_threshold": 0.5319,
"card_precision_at_budget": 0.5613,
"precision_at_budget": 0.6195,
"scenario_recall": {
"1": 0.9161,
"2": 0.6643,
"3": 0.8191
},
"columns": [
"amount",
"hour",
"is_weekend",
"customer_count_1d",
"customer_mean_1d",
"customer_count_7d",
"customer_mean_7d",
"customer_count_30d",
"customer_mean_30d",
"terminal_count_1d",
"terminal_mean_1d",
"terminal_count_7d",
"terminal_mean_7d",
"terminal_count_30d",
"terminal_mean_30d",
"amount_over_customer_mean_1d",
"amount_over_customer_mean_7d",
"amount_over_customer_mean_30d",
"customer_risk_1d",
"customer_risk_count_1d",
"customer_risk_7d",
"customer_risk_count_7d",
"customer_risk_30d",
"customer_risk_count_30d",
"terminal_risk_1d",
"terminal_risk_count_1d",
"terminal_risk_7d",
"terminal_risk_count_7d",
"terminal_risk_30d",
"terminal_risk_count_30d"
]
}
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