Approximation and Projection (UMAP) , or t-distributed Stochastic Neighbour Embedding (t-SNE) , which have found application in organocatalysis to uniformly sample from chemical space . Supervised learning requires labelled data and aims at identifying correlations between the target values and the corresponding input features. A plethora of different supervised learning algorithms are available and a priori knowledge which architecture works best is challenging. Some of the most widely used algorithms include multivariate linear regression (MLR) in which the target is linearly modelled by multiple independent variables. Other notable architectures include decision trees , support vector machines and deep neural networks . While the accuracy of the model is paramount, interpretability is also highly desirable. In this regard, MLR bears the advantage that it yields a directly interpretable function (vide infra) which can be used for mechanistic inference. However, it is important to note that the caveat of correlation and causality must be considered. Also, for other kind of models, e.g., random forests, it is common practice to consider the importance of individual features for the model's prediction to gain mechanistic insight.