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aai540-group3/diabetes-readmission

Port of the diabetes-readmission dataset from UCI (link here). See details there and use carefully. Basic preprocessing done by the imodels team in this notebook. The target is the binary outcome readmitted. Sample usage Load the data: from datasets import load_dataset dataset = load_dataset("imodels/diabetes-readmission") df = pd.DataFrame(dataset['train']) X = df.drop(columns=['readmitted']) y = df['readmitted'].values Fit a model: import imodels import numpy as np m =… See the full description on the dataset page: https://huggingface.co/datasets/aai540-group3/diabetes-readmission.

sourceHugging Faceupdated 2y agoView on Hugging Face

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Port of the diabetes-readmission dataset from UCI (link here). See details there and use carefully.

Basic preprocessing done by the imodels team in this notebook.

The target is the binary outcome readmitted.

Sample usage

Load the data:

from datasets import load_dataset

dataset = load_dataset("imodels/diabetes-readmission")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['readmitted'])
y = df['readmitted'].values

Fit a model:

import imodels
import numpy as np

m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)

Evaluate:

df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['readmitted'])
y_test = df['readmitted'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))