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
File size: 527 Bytes
d49b0eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ---
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)
``` |