地球资源数据云——数据资源详情
国家教育水平和收入
该数据集《Countries Education Level and Income》主要用于多分类任务，数据形态以表格为主，应用场景偏向安全检测。 题目说明：Education index, level for education and the income per country 任务类型：表格多分类。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 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：Country - Income - and - Education - Level.csv。 If this data set benefit your work, don't hesitate to upvote This data is gathered from United Nations databases, the following links below is been used. https://rankedex.com/society - rankings/education - index https://en.wikipedia.org/wiki/Education_Index https://www.un.org/development/desa/dpad/wp - content/uploads/sites/45/WESP2022_ANNEX.pdf This data can be used to measure the influence of education or income or both on any variable or vector, for example, ANOVA models. The Income classification is for year 2021 and the education index is for 2019 to 2023.
摘要概览
该数据集《Countries Education Level and Income》主要用于多分类任务，数据形态以表格为主，应用场景偏向安全检测。 题目说明：Education index, level for education and the income per country
任务类型：表格多分类。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
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：Country - Income - and - Education - Level.csv。
If this data set benefit your work, don't hesitate to upvote
This data is gathered from United Nations databases, the following links below is been used.
https://rankedex.com/society - rankings/education - index https://en.wikipedia.org/wiki/Education_Index https://www.un.org/development/desa/dpad/wp - content/uploads/sites/45/WESP2022_ANNEX.pdf
This data can be used to measure the influence of education or income or both on any variable or vector, for example, ANOVA models.
The Income classification is for year 2021 and the education index is for 2019 to 2023.