rl

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 categorical and
numerical features" benchmark. 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

本数据集用于表格数据基准测试（tabular data benchmark），转换方式与https://github.com/LeoGrin/tabular-benchmark 中的一致。该数据集属于「类别型与数值特征分类」基准任务范畴。原始描述如下：

本次挑战赛旨在向研究社区开放第四范式（4Paradigm）的真实世界实用数据集。尽管各类数据的类型存在差异，但所有数据集均采用统一格式进行组织。数据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）。所有任务均为二分类问题，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已隐藏，但其数据结构已公开。第二阶段的最终得分将为所有测试数据集上排名的平均值，据此生成整体排名，并依据该排名确定最终获胜者。

所有任务均受时间预算限制。Codalab平台为所有参赛者提供共享的计算资源。每份代码提交任务将在具备如下配置的计算节点上执行：2核CPU / 8GB内存 / 40GB SSD固态硬盘，搭载Ubuntu操作系统。为确保评估公平性，代码提交的执行时长将受到严格限制。

http://automl.chalearn.org/data