使用反学习数据集教授关键机器学习原理
Teaching Key Machine Learning Principles Using Anti-learning Datasets
摘要：
机器学习的大部分教学都集中在迭代爬山方法和使用局部知识来获得导致局部或全局最大值的信息。在本文中，我们提倡教学的替代方法，推广到最好的解决方案，包括一种方法称为反学习。通过使用简单的教学方法，学生可以更深入地理解对训练过程中排除的数据进行验证的重要性，并且每个问题都需要自己的方法来解决。我们还证明了使用足够的数据训练模型的要求，表明不同粒度的交叉验证可以产生非常不同的结果。
英文摘要：
Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima. In this paper we advocate the teaching of alternative methods of generalising to the best possible solution, including a method called anti-learning. By using simple teaching methods, students can achieve a deeper understanding of the importance of validation on data excluded from the training process and that each problem requires its own methods to solve. We also exemplify the requirement to train a model using sufficient data by showing that different granularities of cross-validation can yield very different results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
