This is good, but could also be good to mention that you're using umap for dimensionality reduction with cosine metric. https://github.com/Z-Gort/Reservoirs-La

This is good, but could also be good to mention that you're using umap for dimensionality reduction with cosine metric. https://github.com/Z-Gort/Reservoirs-Lab/blob/main/src/electron/scripts/reduceDimension.py https://github.com/Z-Gort/Reservoirs-Lab/blob/main/src/elect... Dimensionality reduction from n >> 2 dimensions to 2 dimensions can be very fickle, so the hyperparameters matter. Your visualization can change significantly significantly depending on choice of metric. https://umap-learn.readthedocs.io/en/latest/parameters.html https://umap-learn.readthedocs.io/en/latest/parameters.html You may want to consider projecting to more than 2 dimensions too. You may ask, how does one visualize more than two dimensions? Through a scatterplot matrix of 2 axes at a time. https://seaborn.pydata.org/examples/scatterplot_matrix.html https://seaborn.pydata.org/examples/scatterplot_matrix.html These are used for PCA-type multivariate analyses to visualize latent variables in higher dimensions than 2, but 2 dimensions at a time. Some clustering behavior that cannot be seen in 2 axes might be seen in higher dimensions. We used to do this our lab to find anomalies in high dimensions.