{ "fetched_at": "2026-10-20T07:06:06Z", "source_url": "https://www.kaggle.com/competitions/de-attrition/data", "pages": [ { "name": "data-description", "content": "

文件描述

\n

train.csv - 训练集
test.csv - 测试集
sampleSubmission.csv - 提交文件格式

问题描述

\n我们有员工的各种统计信息，以及该员工是否已经离职，统计的信息包括了（工资、出差、工作环境满意度、工作投入度、是否加班、是否升职、工资提升比例等）
\n现在需要你来通过训练数据得出员工离职预测，并给出你在测试集上的预测结果。\n我们将给出课程上公开的榜单\n

数据说明

评分标准：AUC

Acknowledgements

数据来自IBM Watson Analytics分析平台分享的样例数据，选取了部分作为训练和测试集

The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:

The evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:

\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]

The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.

Submission Format

For every author in the dataset, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).

The file should contain a header and have the following format:

AuthorId,DuplicateAuthorIds
1,1