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
| { | |
| "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 | |
| } | |
| } |