I have a dataset that has 5 features; 2 continuous and 3 categorical. If I use one hot encoding on each of the categorical features, I end up with some 600 features for each observation. I then use tsne to reduce the dimensionality of the data from ~600 to 2. I plot the tsne results in a scatter plot. You can see some clear patterns and clusters, and if you zoom in, the data in the clusters make sense i.e points in a cluster are related to each other in some way.
My question is this: would it make sense to train a machine learning model, say clustering or some density model, on the 2-d results of the tsne output? Or is that a statistical “no-no” and I should really only use tsne for visualisation and train my model on the original data with 5 features.