Hi
Thanks for the replies. I read about the available functions in the
PCA section. Consider the following code
x = StandardScaler().fit_transform(x)
pca = PCA()
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents)
loadings = pca.components_
finalDf = p
OK, so the normal install is working. Now, to fix your issue we need to
understand how `sc.install_pypi_package` is working and mainly how does it
call `pip`. We need to make sure that it call the right pip (the system
`pip3` in your case).
On Fri, 22 Jan 2021 at 14:39, Bertrand B. wrote:
> Tha
Thank you Guillaume for your help,
I am using : (running on AWS EMR-6.2)
pip3 --version
pip 9.0.3 from /usr/lib/python3.7/site-packages (python 3.7)
pip3 install scikit-learn
Collecting scikit-learn
Using cached
https://files.pythonhosted.org/packages/f4/7b/d415b0c89babf23dcd8ee631015f043e2d7
Hi Mahmood,
There are different pieces of info that you can get from PCA:
1. How important is a given PC to reconstruct the entire dataset -> This
is given by explained_variance_ratio_ as Guillaume suggested
2. What is the contribution of each feature to each PC (remember that a
PC is a line
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
> escribió:
>
>
> I am not really understanding the question, sorry
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 wrote:
> Hi
> I have a question about PCA and that is,
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 wor
@Bertrand Could you tell us which version of `pip` to you use (you need pip
>= 19.0 for manylinux2010 and pip >= 19.3 for manylinux2014)
On Fri, 22 Jan 2021 at 09:49, Guillaume Lemaître
wrote:
> We might experience an issue with PyPI not selecting the manylinux2010
> wheel: https://github.com/sc
We might experience an issue with PyPI not selecting the manylinux2010
wheel: https://github.com/scikit-learn/scikit-learn/issues/19233
We have to check but we will probably shortly upload manylinux1 wheels that
should resolve the issue.
I am curious if fetching the wheel by hand and installing vi
