# Intelligent Feature Selection (with SVM or PCA)

**URL:** https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788
**Category:** Code Sharing
**Created:** [February 23, 2021, 12:59pm UTC](https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788 "2021-02-23T12:59:26Z")
**Posts on this page:** 1
**Showing post:** 13

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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: [February 28, 2021, 10:54pm UTC](https://discourse.flucoma.org/t/intelligent-feature-selection-with-svm-or-pca/788/13 "2021-02-28T22:54:55Z")

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> [@weefuzzy](#):
>
> Hiya, I’ve not read everything in this thread, but you have all the Eigen-doodads you need for this sort of thing, between the bases matrix (eigenvectors of the zero-meanified covariance) and the values array (squareroots of the eigenvalues of same).

Hehe. I somehow understand this even less than when I didn’t understand it when @tedmoore mentioned eigenvalues in the previous post.

Although it’s all quite speculative, the results (as per @tedmoore’s video above) are really promising, and “better than random”, as someone mentioned in their Q&A this past weekend.

From the looks of it, an SVM gives better results than an PCA for this, though the general idea is the same. As a kind of “figuring out which descriptors/stats may be worthwhile using at all”, which is something I was struggling with in [this thread](https://discourse.flucoma.org/t/regression-classification-regressification/547), where I arbitrarily (and tediously) tested all kinds of permutations trying to arrive at a similar conclusion.

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