地球资源数据云——数据资源详情
动物园动物分类
该数据集《Zoo Animal Classification》主要用于多分类任务，数据形态以表格为主。 题目说明：Use Machine Learning Methods to Correctly Classify Animals Based Upon Attributes 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：class.csv, zoo.csv。 This dataset consists of 101 animals from a zoo. There are 16 variables with various traits to describe the animals. The 7 Class Types are: Mammal, Bird, Reptile, Fish, Amphibian, Bug and Invertebrate The purpose for this dataset is to be able to predict the classification of the animals, based upon the variables. It is the perfect dataset for those who are new to learning Machine Learning. zoo.csv Attribute Information: (name of attribute and type of value domain) 1. animal_name: Unique for each instance
摘要概览
该数据集《Zoo Animal Classification》主要用于多分类任务，数据形态以表格为主。 题目说明：Use Machine Learning Methods to Correctly Classify Animals Based Upon Attributes
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：class.csv, zoo.csv。
This dataset consists of 101 animals from a zoo. There are 16 variables with various traits to describe the animals. The 7 Class Types are: Mammal, Bird, Reptile, Fish, Amphibian, Bug and Invertebrate
The purpose for this dataset is to be able to predict the classification of the animals, based upon the variables. It is the perfect dataset for those who are new to learning Machine Learning.
zoo.csv
Attribute Information: (name of attribute and type of value domain)
1. animal_name: Unique for each instance