Instructions to use pfizer-project-team/binary-segA-vs-segBC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use pfizer-project-team/binary-segA-vs-segBC with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("pfizer-project-team/binary-segA-vs-segBC", "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: 726 Bytes
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"best_model_name": "HistGradientBoosting",
"best_threshold": 0.45,
"task": "SEG_A vs SEG_BC",
"label_mapping": {
"0": "SEG_A",
"1": "SEG_BC"
},
"test_metrics": {
"model": "HistGradientBoosting",
"threshold": 0.45,
"accuracy": 0.7722689075630252,
"macro_f1": 0.7703775750550399,
"weighted_f1": 0.7719711978163979,
"roc_auc": 0.8518300292864351,
"SEG_A_precision": 0.7809885931558935,
"SEG_A_recall": 0.8017174082747853,
"SEG_A_f1": 0.7912172573189522,
"SEG_BC_precision": 0.7615023474178404,
"SEG_BC_recall": 0.737943585077343,
"SEG_BC_f1": 0.7495378927911276
}
} |