Tabular Classification
Scikit-learn
English
hierarchical
healthcare
ehr
copd
clinical-risk
tabular
scikit-learn
clustering
unsupervised
Instructions to use stormid/copd-model-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use stormid/copd-model-e with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("stormid/copd-model-e", "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
| # Reduction | |
| This folder contains scripts for combining, reducing, filling and scaling processed EHR data for modelling. Scripts should be run in the below order. | |
| Note that scripts must be run in the below order: | |
| 1. `combine.py` - combine datasets and perform any post-processing | |
| 2. `post_prod_reduction.py` - Combine columns to reduce 0 values | |
| 3. `remove_ids.py` - remove receiver, scale up and test IDs | |
| 4. `clean_and_scale_train.py` - impute nulls and min-max scale training data | |
| 5. `clean_and_scale_test.py` - impute nulls and min-max scale testing data | |
| _NB: The data_type in `clean_and_scale_test.py` can be changed to rec, sup, val and test._ |