地球资源数据云——数据资源详情
简单的性别分类
该数据集《Simple Gender Classification》主要用于多分类任务，数据形态以表格为主。 题目说明：Let's classify gender 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：gender.csv。 The 10 columns in this template are: ID: unique identifier for each individual in the dataset Gender: male or female Age: age in years Height (cm): height in centimeters Weight (kg): weight in kilograms Occupation: profession or job title Education Level: highest level of education attained Marital Status: single, married, divorced, widowed, etc. Income (USD): annual income in US dollars Favorite Color: favorite color of the individual (this column is just for fun and has no real meaning) This dataset can be used for various classification and data analysis tasks, such as predicting an individual's gender or income level based on their occupation, education level, and other demographic factors.
摘要概览
该数据集《Simple Gender Classification》主要用于多分类任务，数据形态以表格为主。 题目说明：Let's classify gender
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：gender.csv。
The 10 columns in this template are:
ID: unique identifier for each individual in the dataset Gender: male or female Age: age in years Height (cm): height in centimeters Weight (kg): weight in kilograms Occupation: profession or job title Education Level: highest level of education attained Marital Status: single, married, divorced, widowed, etc.
Income (USD): annual income in US dollars Favorite Color: favorite color of the individual (this column is just for fun and has no real meaning)
This dataset can be used for various classification and data analysis tasks, such as predicting an individual's gender or income level based on their occupation, education level, and other demographic factors.