5. Re: Question about LDA's coef_ attribute (Serafeim Loukas)
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Message: 1
Date: Sun, 15 Oct 2017 18:42:56 -0700
From: Ismael Lemhadri mailto:lemha...@stanford.edu>>
To: scikit-learn@python.org <mailto:scikit-learn@python.org>
Subject: [scikit-learn] unclear help
On 16/10/17 17:16, Ismael Lemhadri wrote:
My concern is actually not about not mentioning the scaling but about
not mentioning the centering.
That is, the sklearn PCA removes the mean but it does not mention it in
the help file.
I think it's currently assumed given the definition of the PCA, bu
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>
> Message: 1
> Date: Sun, 15 Oct 2017 18:42:56 -0700
> From: Ismael Lemhadri
> To: scikit-learn@python.org
> Subject: [scikit-learn] unclear help file for
> sklearn.decomposition.pca
> Message-ID
Ismael,
as far as I saw the sklearn.decomposition.PCA doesn't mention scaling at
all (except for the whiten parameter which is post-transformation scaling).
So since it doesn't mention it, it makes sense that it doesn't do any
scaling of the input. Same as np.linalg.svd.
You can verify that
Dear all,
The help file for the PCA class is unclear about the preprocessing
performed to the data.
You can check on line 410 here:
https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/
decomposition/pca.py#L410
that the matrix is centered but NOT scaled, before performing the singula
