Hi Mahmood, I believe your question is answered here: https://stackoverflow.com/questions/22984335/recovering-features-names-of-explained-variance-ratio-in-pca-with-sklearn
> El 22 ene 2021, a las 10:26, Guillaume Lemaître <g.lemaitr...@gmail.com> > escribió: > > > I am not really understanding the question, sorry. > Are you seeking for the `explained_variance_ratio_` attribute that give you a > relative value of the eigenvalues associated to the eigenvectors? > >> On Fri, 22 Jan 2021 at 10:16, Mahmood Naderan <mahmood...@gmail.com> wrote: >> Hi >> I have a question about PCA and that is, how we can determine, a >> variable, X, is better captured by which factor (principal >> component)? For example, maybe one variable has low weight in the >> first PC but has a higher weight in the fifth PC. >> >> When I use the PCA from Scikit, I have to manually work with the PCs, >> therefore, I may miss the point that although a variable is weak in >> PC1-PC2 plot, it may be strong in PC4-PC5 plot. >> >> Any comment on that? >> >> Regards, >> Mahmood >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > > > -- > Guillaume Lemaitre > Scikit-learn @ Inria Foundation > https://glemaitre.github.io/ > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn
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