Instructions to use eulerlab/gcl_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use eulerlab/gcl_classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("eulerlab/gcl_classifier", "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
| license: mit | |
| tags: | |
| - sklearn | |
| - random-forest | |
| - calibrated-classifier | |
| library_name: sklearn | |
| # Calibrated Random Forest Classifier | |
| This is a calibrated Random Forest classifier trained with scikit-learn on two-photon (2p) ganglion cell layer (GCL) responses from the mouse retina. | |
| The training data is from Baden et al. 2016 (https://datadryad.org/dataset/doi:10.5061/dryad.d9v38). | |
| ## Usage | |
| ```python | |
| from gcl_classifier import model | |
| predictions = model.predict(X_test) | |
| probabilities = model.predict_proba(X_test) | |
| ``` |