地球资源数据云——数据资源详情
该数据集《Student Flexibility in Online Learning》主要用于监督学习任务,数据形态以表格为主。 题目说明:Effectiveness of online education 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:students_adaptability_level_online_education.csv。 Effectiveness of online education Since as a beginner in machine learning it would be a great opportunity to try some techniques to predict the outcome of Students’ Adaptability Level Prediction in Online Education using Machine Learning Approaches

该数据集《Student Flexibility in Online Learning》主要用于监督学习任务,数据形态以表格为主。 题目说明:Effectiveness of online education
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:students_adaptability_level_online_education.csv。
Effectiveness of online education Since as a beginner in machine learning it would be a great opportunity to try some techniques to predict the outcome of Students’ Adaptability Level Prediction in Online Education using Machine Learning Approaches
该数据集《Student Flexibility in Online Learning》主要用于监督学习任务,数据形态以表格为主。
数据格式为 CSV。
在本页登录后即可下载。建议引用格式:地球资源数据云. 学生在线学习的灵活性. https://www.gis5g.com/dataset/2031995838400991234