Sklearn PCA explained variance and explained variance ratio difference
It's just normalization to see how each principal component important. You can say:
explained_variance_ratio_ = explained_variance_/np.sum(explained_variance_)
I'm trying to get the variances from the eigen vectors.
What is the difference between explained_variance_ratio_ and explained_variance_ in PCA?