PMLB (Penn Machine Learning Benchmarks)

Penn Machine Learning Benchmarks (PMLB) 是一组经过精心策划的大型基准数据集，用于评估和比较有监督的机器学习算法。这些数据集涵盖了广泛的应用，包括二元/多类分类问题和回归问题，以及分类、有序和连续特征的组合。

Penn Machine Learning Benchmarks (PMLB) is a carefully curated collection of large benchmark datasets for evaluating and comparing supervised machine learning algorithms. These datasets cover a wide range of applications, including binary/multi-class classification problems, regression tasks, as well as combinations of nominal, ordinal, and continuous features.

搜集汇总

背景与挑战

背景概述

PMLB是一个由宾夕法尼亚大学于2017年发布的大型机器学习基准数据集集合，专门用于有监督学习算法的评估和比较，包含多种分类和回归问题及不同特征类型的数据集。