albert

Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical features" benchmark.

Original link: https://openml.org/d/41147

Original description:

The goal of this challenge is to expose the research community to real world datasets of interest to 4Paradigm. All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are welcome to use additional feature extraction procedures (as long as they do not violate any rule of the challenge). All problems are binary classification problems and are assessed with the normalized Area Under the ROC Curve (AUC) metric (i.e. 2*AUC-1).
The identity of the datasets and the type of data is concealed, though its structure is revealed. The final score in phase 2 will be the average of rankings on all testing datasets, a ranking will be generated from such results, and winners will be determined according to such ranking.
The tasks are constrained by a time budget. The Codalab platform provides computational resources shared by all participants. Each code submission will be exceuted in a compute worker with the following characteristics: 2Cores / 8G Memory / 40G SSD with Ubuntu OS. To ensure the fairness of the evaluation, when a code submission is evaluated, its execution time is limited in time.
http://automl.chalearn.org/data

本数据集源自表格数据基准测试（tabular data benchmark）平台https://github.com/LeoGrin/tabular-benchmark，且采用与该平台一致的预处理流程。本数据集属于「数值与分类特征联合分类」基准任务范畴。

原始数据集链接：https://openml.org/d/41147

原始数据集描述：

本挑战赛旨在向学术界展示第四范式（4Paradigm）关注的真实世界数据集。所有数据集均采用统一格式封装，尽管其数据类型存在差异。数据集以预处理后的矩阵形式提供，以便参赛者专注于分类任务，不过参赛者也可自行采用额外的特征提取流程（只要不违反挑战赛的任何规则）。所有任务均为二元分类任务，评估指标采用归一化受试者工作特征曲线下面积（normalized Area Under the ROC Curve, AUC），即2*AUC-1。

数据集的具体身份与数据类型均未公开，仅披露其数据结构。第二阶段的最终得分为所有测试数据集上排名的平均值，据此生成整体排名，并以此决出优胜者。

本次任务受时间预算约束。Codalab平台为所有参赛者提供共享的计算资源。每份提交的代码将在具备如下配置的计算节点中运行：2核CPU、8GB内存、40GB固态硬盘，搭载Ubuntu操作系统。为保障评估公平性，每份提交代码的运行时长将受到严格限制。

http://automl.chalearn.org/data