Tabular Classification
Scikit-learn
Joblib
genomics
structural-variants
short-tandem-repeats
variant-calling
confidence-calibration
random-forest
Instructions to use khyeom/SVSTR-Score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use khyeom/SVSTR-Score with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("khyeom/SVSTR-Score", "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
| { | |
| "release_version": "1.0", | |
| "tiers": { | |
| "HIGH": "CS>=0.70", | |
| "MODERATE": "0.50<=CS<0.70", | |
| "WARNING": "0.30<=CS<0.50", | |
| "LOW": "CS<0.30" | |
| }, | |
| "score": "CS = isotonic-calibrated probability of concordance with long-read truth", | |
| "calibration": "isotonic on OOF; SV Brier 0.0771->0.0744, STR 0.1673->0.1589", | |
| "note": "Tiers are buckets of the calibrated CS. HIGH is the candidate-triage tier." | |
| } |