aai540-group3/diabetes-readmission
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--- annotations_creators: [] language: [] language_creators: [] license: [] multilinguality: [] pretty_name: diabetes-readmission size_categories: - 100K<n<1M source_datasets: [] tags: - interpretability - fairness - medicine task_categories: - tabular-classification task_ids: [] --- Port of the diabetes-readmission dataset from UCI (link [here](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)). See details there and use carefully. Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb). 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)) ```
annotations_creators: 无 language: 无 language_creators: 无 license: 无 multilinguality: 无 pretty_name: 糖尿病再入院（diabetes-readmission） size_categories: - 样本量范围：10万 < 样本量 < 100万 source_datasets: 无 tags: - 可解释性（interpretability） - 公平性（fairness） - 医学（medicine） task_categories: - 表格分类（tabular-classification） task_ids: 无 本数据集为UCI机器学习库中糖尿病再入院（diabetes-readmission）数据集的移植版本，源链接见[此处](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)，请参阅源站点获取详细信息并谨慎使用。 基础预处理工作由[imodels团队](https://github.com/csinva/imodels)在[该Notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb)中完成。 本次任务的预测目标为二分类结果「再入院（readmitted）」。 ### 示例用法 加载数据： python 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 拟合模型： python import imodels import numpy as np m = imodels.FIGSClassifier(max_rules=5) m.fit(X, y) print(m) 模型评估： python 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))
