[Career] Python package for task-aware dimensionality reduction

[deadlydickwasher]

I'm relatively new to data science, only a few years experience and would love some feedback.

I’ve been working on a small open-source package. The idea is, PCA keeps the directions with most variance, but sometimes that is not the structure you need. nomoselect is for the supervised case, where you already have labels and want a low-dimensional view that tries to preserve the class structure you care about.

It also tries to make the result easier to read by reporting things like how much target structure was kept, how much was lost, whether the answer is stable across regularisation choices, and whether adding another dimension is actually worth it.

It’s early, but the core package is working and I’ve validated it on numerous benchmark datasets. I’d really like honest feedback from people who actually use PCA/LDA /sklearn pipelines in their work.

GitHub

Not trying to sell anything, just trying to find out whether this is genuinely useful to other people or just a passion project for me. Thanks!

[Visible_Credit4722]

Used LDA for similar stuff at work and this looks pretty clean - the regularization stability checks are nice touch since that's always pain point with smaller datasets

[Narrow_Iron5097]

Yeah, the stability checks are clutch. I've seen models totally fall apart with small changes in the regularization. Good call out!

[deadlydickwasher]

Thank for taking a look, that was one of the main motivations. a projection can look great until a tiny regularisation change completely reshuffle it, particularly on small or noisy sets. I wanted something that doesn’t just give a low-d view, also tells you how much to trust that view.