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Trying to tune the parameters of

sklearn.manifold.TSNE(n_components=2, *, perplexity=30.0, early_exaggeration=12.0, learning_rate=200.0, n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, metric='euclidean', init='random', verbose=0, random_state=None, method='barnes_hut', angle=0.5, n_jobs=None, square_distances='legacy')

even I tried a number of combinations, the visual is not showing a clear seperation between two classes. Is there a way to tune tsne automatically or manually to find the best parameters?

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  • $\begingroup$ Maybe there isn’t much of a separation between your two classes, at least not on the features you have. That would be a valuable insight. $\endgroup$
    – Dave
    Sep 18 at 12:14

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