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.

中文翻译：

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使用反学习数据集教授关键的机器学习原理

机器学习的大部分教学重点是迭代爬山方法以及使用本地知识来获取导致局部或全局最大值的信息。在本文中，我们提倡使用替代方法来推广到最佳解决方案，其中包括一种称为反学习的方法。通过使用简单的教学方法，学生可以更深入地了解对从培训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使用自己的方法来解决。我们还通过显示不同的交叉验证粒度可以产生非常不同的结果，来说明使用足够的数据训练模型的要求。