I have been utilizing the PCA class from sklearn.decomposition to reduce the dimensionality of my feature space for visualization purposes.
I have a question about the outcome: Upon implementing the fit and transform functions of the PCA class, I receive an array named X_transformed with shape (n_samples, n_components), as mentioned in the documentation. Is the order of columns in the X_transformed array sorted by the amount of variance explained? The documentation notes that PCA.components_ is ordered by explained variance, so I assume that the columns in X_transformed follow suit. Please correct me if I am mistaken.
Here's a quick example:
from sklearn.decomposition import PCA
pca = PCA()
pca.fit(X) # X represents an array containing my original features. X.shape=(n_samples, n_features)
X_transformed = pca.transfom(X) # Now, does the order of columns in X_transformed indicate explained variance?
Thank you!