# Regression + Classification = Regressification?

**URL:** <https://discourse.flucoma.org/t/regression-classification-regressification/547>\
**Category:** Pre-Release Toolbox2 New Ideas\
**Created:** [July 4, 2020, 5:14pm UTC](https://discourse.flucoma.org/t/regression-classification-regressification/547 "2020-07-04T17:14:49Z")\
**Posts on this page:** 1\
**Showing post:** 38

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**Author:** ![rodrigo.constanzo](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/rodrigo.constanzo/32/12_2.png) [@rodrigo.constanzo](https://discourse.flucoma.org/u/rodrigo.constanzo)\
**Post date:** [July 25, 2020, 8:26pm UTC](https://discourse.flucoma.org/t/regression-classification-regressification/547/38 "2020-07-25T20:26:46Z")

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I returned to this testing today, with the realization that I can’t have \> 9 dimensions (with my current training set) [due to how PCA works](https://discourse.flucoma.org/t/getting-negative-numbers-from-fluid-normalize/595/9).

I’ve changed up the analysis now too and am taking 25 MFCCs (and leaving out the 0th coefficient), as well as two derivatives. So an initial `fluid.dataset~` with 196 dimensions in it. So a nice chunky one…

I tested seeing how small I can get things and have OK matching.

> 196d → 2d = 28.9%  
> 196d → 5d = 41.9%  
> 196d → 8d = 44.1%  
> 196d → 9d = 54.0%

Not quite the 67.05% I was getting with a 12d reduction (which is surprising as a couple of those dimensions would be dogshit based on [the pseudo-bug of requesting more dimensions than points](https://discourse.flucoma.org/t/getting-negative-numbers-from-fluid-normalize/595/9)).

I also made another training set with 120 points in it, but I can’t as easily verify the validity of the matching since it’s essentially a whole set of different sounding attacks on the snare. So I’ll go back and create a new set of training and testing data where I have something like 20 discrete hits in it, so I can test PCA going up to that many dimensions.

I’ll also investigate to see what I did to get that 67% accuracy above, to see if I build off that. But having more MFCCs and derivs seems promising at the moment, particularly if I can squeeze the shit out of it with PCA.

**edit:**  
It turns out I got 67.05% when running the matching with no dimensionality reduction at all. If I do the same with the 196d variant (25MFCC + 2derivs) I get 73.9% matching out of the gate. So sans reduction, this is working better.

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