地球资源数据云——数据资源详情
机器预测维护分类
该数据集《Machine Predictive Maintenance Classification》主要用于二分类任务，数据形态以表格为主。 题目说明：Dataset to predict machine failure (binary) and type (multiclass) 任务类型：表格二分类。 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：predictive_maintenance.csv。 Machine Predictive Maintenance Classification Dataset Since real predictive maintenance datasets are generally difficult to obtain and in particular difficult to publish, we present and provide a synthetic dataset that reflects real predictive maintenance encountered in the industry to the best of our knowledge. The dataset consists of 10 000 data points stored as rows with 14 features in columns UID: unique identifier ranging from 1 to 10000 productID: consisting of a letter L, M, or H for low (50% of all products), medium (30%), and high (20%) as product quality variants and a variant - specific serial number
摘要概览
该数据集《Machine Predictive Maintenance Classification》主要用于二分类任务，数据形态以表格为主。 题目说明：Dataset to predict machine failure (binary) and type (multiclass)
任务类型：表格二分类。
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：predictive_maintenance.csv。
Machine Predictive Maintenance Classification Dataset
Since real predictive maintenance datasets are generally difficult to obtain and in particular difficult to publish, we present and provide a synthetic dataset that reflects real predictive maintenance encountered in the industry to the best of our knowledge.
The dataset consists of 10 000 data points stored as rows with 14 features in columns
UID: unique identifier ranging from 1 to 10000
productID: consisting of a letter L, M, or H for low (50% of all products), medium (30%), and high (20%) as product quality variants and a variant - specific serial number