Reducing dimensionality via PCA before training is a common practice, but PCA cannot makes use of nonlinear relations between features.
I read about UMAP (e.g. https://adanayak.medium.com/dimensionality-reduction-using-uniform-manifold-approximation-and-projection-umap-4aa4cef43fed), a technique for reducing dimensionality that is able to make sense of nonlinear relations between features.
However, I only saw its use in data presentation and exploration. Would it make sense to use UMAP as a form of feature engineering/dimensionality reduction when creating input for downstream model training?