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
File size: 649 Bytes
53a6def | 1 2 3 4 5 6 7 8 9 10 11 12 | # 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._ |