Chemical data must be encoded for the use in machine-learning algorithms. In this article, we present a selection of featurization methods. Furthermore, we give an insight into the field of application for which individual methods can be used. The introduction to this topic is facilitated by the presentation of a code example. Our goal was to provide a step-by-step tutorial that provides a basic understanding of ML in chemistry. For this purpose, a dataset consisting of partition coefficients for the extraction of dimethoxymethanol from an aqueous system is provided. We could show that the way of featurizing this dataset plays a crucial role for model performance.

化学数据必须经过编码才能用于机器学习算法。在本文中，我们介绍了一系列特征化方法。此外，我们深入了解了可以使用单个方法的应用领域。代码示例的介绍有助于对该主题的介绍。我们的目标是提供一个循序渐进的教程，让您对化学中的机器学习有一个基本的了解。为此，提供了一个数据集，该数据集由用于从水系统中提取二甲氧基甲醇的分配系数组成。我们可以证明，对这个数据集进行特征化的方式对模型性能起着至关重要的作用。