A deeper look into the principal components

Before we take a look at our second feature transformation algorithm, it is important to take a look at how principal components are interpreted:

  1. Our iris dataset is a 150 x 4 matrix, and when we calculated our PCA components when n_components was set to 2, we obtained a components matrix of size 2 x 4:
# how to interpret and use components pca.components_ # a 2 x 4 matrix  array([[ 0.52237162, -0.26335492, 0.58125401, 0.56561105], [ 0.37231836, 0.92555649, 0.02109478, 0.06541577]])
  1. Just like in our manual example of calculating eigenvectors, the components_ attribute can be used to project data using matrix multiplication. We do so by multiplying our original dataset with the transpose of the ...

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