Instructions to use jrosenzw/autotrain-diabetes-detection-2-74371139581 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use jrosenzw/autotrain-diabetes-detection-2-74371139581 with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("jrosenzw/autotrain-diabetes-detection-2-74371139581", "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
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 74371139581
- CO2 Emissions (in grams): 0.6961
Validation Metrics
- Loss: 0.468
- Accuracy: 0.753
- Precision: 0.667
- Recall: 0.593
- AUC: 0.839
- F1: 0.627
Usage
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
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