Sklearn PCA is pca.components_ the loadings?
pca.components_
is the orthogonal basis of the space your projecting the data into. It has shape (n_components, n_features)
. If you want to keep the only the first 3 components (for instance to do a 3D scatter plot) of a datasets with 100 samples and 50 dimensions (also named features), pca.components_
will have shape (3, 50)
.
I think what you call the "loadings" is the result of the projection for each sample into the vector space spanned by the components. Those can be obtained by calling pca.transform(X_train)
after calling pca.fit(X_train)
. The result will have shape (n_samples, n_components)
, that is (100, 3)
for our previous example.