scpf

Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : This is a pre-processed version of the dataset used in Kaggles See Click Predict Fix competition (Kaggle 2013). It concerns the prediction of three target variables that represent the number of views, clicks and comments that a specific 311 issue will receive. The issues have been collected from 4 cities (Oakland, Richmond, New Haven, Chicago) in the US and span a period of 12 months (01 2012-12 2012). The version of the dataset that we use here is a random 1 percent sample of the data. In terms of features we use the number of days that an issues stayed online, the source from where the issue was created (e.g. android, iphone, remote api, etc.), the type of the issue (e.g. graffiti, pothole, trash, etc.), the geographical co-ordinates of the issue, the city it was published from and the distance from the city center. All multi-valued nominal variables were first transformed to binary and then rare binary variables (being true for less than 1 percent of the cases) were removed.本数据集为多变量回归数据集，来源：https://link.springer.com/article/10.1007%2Fs10994-016-5546-z：本数据集是Kaggle 2013年「See Click Predict Fix」竞赛所用原始数据集的预处理版本。其任务为预测三类目标变量，分别对应某条311服务工单所能获得的浏览量、点击量与评论数。该数据集的工单采集自美国四座城市（奥克兰、里士满、纽黑文、芝加哥），时间跨度为2012年1月至2012年12月，共计12个月。本次实验使用的数据集版本为原始数据的随机1%采样子集。特征维度方面，本数据集包含工单在线停留时长、工单创建渠道（如安卓、苹果、远程API等）、工单类型（如涂鸦、路面坑洼、垃圾清运等）、工单的地理坐标、发布城市以及距城市中心的距离。所有多值名义变量均先被转换为二值变量，随后移除出现频率低于1%的稀有二值变量。