地球资源数据云——数据资源详情
2019 年糖尿病数据集
该数据集《Diabetes Dataset 2019》主要用于二分类任务，数据形态以文本为主。 题目说明：Prediction and classification using Machine Learning 任务类型：文本二分类。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：diabetes_dataset__2019.csv。 Context This dataset was collected by Neha Prerna Tigga and Dr. Shruti Garg of the Department of Computer Science and Engineering, BIT Mesra, Ranchi - 835215 for research, non - commercial purposes only. An article is also published implementing this dataset. For more information and citation of this dataset please refer: Tigga, N. P., & Garg, S. (2020). Prediction of Type 2 Diabetes using Machine Learning Classification Methods. Procedia Computer Science, 167, 706 - 716. DOI: https://doi.org/10.1016/j.procs.2020.03.336 Content There is a total of 952 instances with 17 independent predictor variables and one binary target or dependent variable, Diabetes.
摘要概览
该数据集《Diabetes Dataset 2019》主要用于二分类任务，数据形态以文本为主。 题目说明：Prediction and classification using Machine Learning
任务类型：文本二分类。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：diabetes_dataset__2019.csv。
Context
This dataset was collected by Neha Prerna Tigga and Dr. Shruti Garg of the Department of Computer Science and Engineering, BIT Mesra, Ranchi - 835215 for research, non - commercial purposes only. An article is also published implementing this dataset. For more information and citation of this dataset please refer:
Tigga, N. P., & Garg, S. (2020). Prediction of Type 2 Diabetes using Machine Learning Classification Methods. Procedia Computer Science, 167, 706 - 716. DOI: https://doi.org/10.1016/j.procs.2020.03.336
Content
There is a total of 952 instances with 17 independent predictor variables and one binary target or dependent variable, Diabetes.