The inclusion of the 8192-feature long PLEC vectors compromises the performance of the RFs, therefore we employed dimensionality reduction. In McInnes et al. an algorithm was developed to improve the problem of dimensionality reduction by generalizing linear approaches like principal component analysis so that they become sensitive to possible nonlinear structures in the data and independent on explained variance. The algorithm, uniform manifold approximation, and projection (UMAP) were developed using Riemannian geometry and algebraic topology. At first, a graph is created with respect to the distances on the underlying topology and to the k-neighborhood of each element. Then, a Laplacian eigenmaps dimensionality reduction method is applied to the graph, which is subsequently modified by a force-directed graph-layout algorithm that minimizes the cross-entropy between the modified graph and the original one. The resulting low-dimensional data representation is therefore optimized to preserve both the local and global structure of the original data. Compared to PCA, UMAP can capture nonlinear structures in high-dimensional data, which is a desirable characteristic when chemical structures are in the underlying data. When comparing to Autoencoder, one needs less coding effort to UMAP, while both rely on non-linear relationships while latent structure information content .