论文标题
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
论文作者
论文摘要
机器学习的大部分教学都集中在迭代爬山方法上，并使用本地知识来获取通往本地或全球最大值的信息。在本文中，我们主张将推广的替代方法教导到最佳解决方案，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验证对从培训过程中排除的数据的重要性，并且每个问题都需要自己的方法来解决。我们还通过表明不同的交叉验证粒度可以产生非常不同的结果来体现使用足够数据训练模型的要求。
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.
