Re: [scikit-learn] Should we standardize data before PCA?

https://stats.stackexchange.com/questions/69157/why-do-we-need-to-normalize-data-before-principal-component-analysis-pca On Thursday, May 24, 2018, 4:41:07 PM PDT, Shiheng Duan wrote: Hello all, I wonder is it necessary or correct to do z score transformation

Re: [scikit-learn] Should we standardize data before PCA?

Hi, that totally depends on the nature of your data and whether the standard deviation of individual feature axes/columns of your data carry some form of importance measure. Note that PCA will bias its loadings towards columns with large standard deviations all else being held equal (meaning that

[scikit-learn] Should we standardize data before PCA?

Hello all, I wonder is it necessary or correct to do z score transformation before PCA? I didn't see any preprocessing for face image in the example of Faces recognition example using eigenfaces and SVMs, link:

Re: [scikit-learn] (no subject)

I did some more tests.  My issue that I brought up may be related to the custom kernel.  On Thursday, May 24, 2018, 12:49:34 PM PDT, Gael Varoquaux wrote: On Thu, May 24, 2018 at 09:35:00PM +0530, aijaz qazi wrote: > scikit- multi learn is misleading.

Re: [scikit-learn] (no subject)

On Thu, May 24, 2018 at 09:35:00PM +0530, aijaz qazi wrote: > scikit- multi learn is misleading. Yes, but I am not sure what scikit-learn should do about this. Gaël ___ scikit-learn mailing list scikit-learn@python.org

[scikit-learn] Support Vector Regression

I have an SVR model that uses custom kernel as follows: 1) sgk = dual_laplace_gaussian_swarm(ss) svr_cust_sig = SVR(kernel=sgk, C=C_Value, epsilon = epsilon_value) svr_fit = svr_cust_sig.fit(X, y) #X is an array shape is [93, 24]  where each row is a time in the columns are variables for the

[scikit-learn] (no subject)

scikit- multi learn is misleading. *Regards,* *Aijaz A.Qazi * ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn