地球资源数据云——数据资源详情
教育不平等数据
该数据集《Education Inequality Data》主要用于监督学习任务，数据形态以表格为主，应用场景偏向文本内容分析。 题目说明：School funding, performance, and access gaps across public, private, and charter 任务类型：表格监督学习。 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：education_inequality_data.csv。 This dataset explores education inequality across 1,000 schools in the United States. It includes key indicators such as funding per student, average test scores, student - teacher ratios, low - income and minority student percentages, internet access levels, and dropout rates. The goal is to help researchers and machine learning engineers: Identify underserved or at - risk school districts Predict dropout risks using demographic and resource indicators Explore the relationship between funding and academic outcomes
摘要概览
该数据集《Education Inequality Data》主要用于监督学习任务，数据形态以表格为主，应用场景偏向文本内容分析。 题目说明：School funding, performance, and access gaps across public, private, and charter
任务类型：表格监督学习。
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：education_inequality_data.csv。
This dataset explores education inequality across 1,000 schools in the United States. It includes key indicators such as funding per student, average test scores, student - teacher ratios, low - income and minority student percentages, internet access levels, and dropout rates.
The goal is to help researchers and machine learning engineers:
Identify underserved or at - risk school districts
Predict dropout risks using demographic and resource indicators
Explore the relationship between funding and academic outcomes