Explained Variance of Each PC

Question:

We mentioned that PCA will order the principal components, with the first PC giving the direction of maximal variance, second PC has second-largest variance, and so on. How much of the variance is explained by the first principal component? The second?

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INSTRUCTOR NOTE:

We found that sometimes the pillow module (which is being used in this example) can cause trouble. If you get an error related to the fetch_lfw_people() command, try the following:

pip install --upgrade PILLOW

If you run into a different error, note that there are changes in one of the parameters for the SVC function called on line 94 of pca/eigenfaces.py . For the 'class_weight' parameter, the argument string "auto" is a valid value for sklearn version 0.16 and prior, but will be depreciated by 0.19. If you are running sklearn version 0.17 or later, the expected argument string should be "balanced". If you get an error or warning when running pca/eigenfaces.py , make sure that you have the correct argument on line 98 that matches your installed version of sklearn.

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