In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels, we illustrate the profound impact of dataset quality on model training and performance. We further introduce two additional datasets designed to represent specific data conditions - one maximizing entropy and the other demonstrating high redundancy. Our findings underscore the importance of appropriate feature selection, adequate data volume, and data quality in achieving high-performing machine learning models. To aid researchers and practitioners, we propose a comprehensive framework for dataset quality assessment, which can help evaluate if the dataset at hand is sufficient and of the required quality for specific tasks. This research offers valuable insights into data assessment practices, contributing to the development of more accurate and robust machine learning models.

翻译：本文深入探讨了机器学习分类任务中数据集质量评估的关键问题。通过利用九个具有不同复杂度的分类任务数据集，我们阐述了数据集质量对模型训练与性能的深远影响。进一步引入了两个针对特定数据条件设计的数据集——一个最大化熵，另一个展示高冗余性。研究结果强调了合适的特征选择、充足的数据量以及数据质量对于实现高性能机器学习模型的重要性。为帮助研究人员和实践者，我们提出了一个全面的数据集质量评估框架，可评估当前数据集是否充分且满足特定任务所需的质量要求。本研究为数据评估实践提供了宝贵见解，有助于开发更准确、更鲁棒的机器学习模型。