Python scikit learn pca.explained_variance_ratio_ cutoff

Question

When choosing the number of principal components (k), we choose k to be the smallest value so that for example, 99% of variance, is retained.

However, in the Pytho

Answer 1

This worked for me with even less typing in the PCA section. The rest is added for convenience. Only 'data' needs to be defined in an earlier stage.

import sklearn as sl
from sklearn.preprocessing import StandardScaler as ss
from sklearn.decomposition import PCA 

st = ss().fit_transform(data)
pca = PCA(0.80)
pc = pca.fit_transform(st) # << to retain the components in an object
pc

#pca.explained_variance_ratio_
print ( "Components = ", pca.n_components_ , ";\nTotal explained variance = ",
      round(pca.explained_variance_ratio_.sum(),5)  )