AllenNeuralDynamics / AllenNeuralDynamics/lamf-analysis
Encoding models
Ouverte
- Langage dominant
- Jupyter Notebook
- Étoiles
- 1
- Forks
- 2
- Merge moyen
- 5 h 28 min
- PR mergées (30 j)
- 2
Description
- [ ] Test/compare boosting vs GLM
- [ ] XGBoost / LightGBM, processing time stats and comparison: To decide if either can be used practically.
- If boosting can be used within reasonable time, then use them to quantify encoding and for feature engineering
- If they cannot be used, then focus on refining GLM and feature engineering using a priority knowledge (best guess)
- Data: Using Visual Behavior dataset (+ previous GAD2-cre GLM fitting results)
Guide de contribution
Aucun guide de contribution indexé pour ce dépôt
Évaluation
Cette issue n'a pas encore été évaluée.