Non-parametric models such as k-nearest neighbors (KNN) make no assumptions about the distribution of data from which a sample is drawn. The algorithm computes the distances from the nearest number of datapoints (k) around the new datapoint (i.e. test set) to predict the property of the new datapoint. The distance algorithm and k can be user-defined or vary based on the local density of points. KNN is simple and versatile but can be computationally expensive for large datasets. While KNN is most often used for classification tasks, it can also be employed for regression. In regression, KNN predicts the average target value of the k nearest points in the training set. Because the KNN algorithm simply predicts the average of the nearest data points, it performs remarkably well across many different representations. In fact, its performance is comparable to or better than much more complex models in the cases of OHE (R 2 = 0.31±0.03), material properties (R 2 = 0.43±0.03), SMILES and SELFIES (R 2 = 0.41±0.03 and 0.41±0.04, respectively), and even graph embeddings (R 2 = 0.43±0.03).