如何在受监督的ML中解释PCA结果

Question

So I have a data set of 700 texts annotated by difficulty levels. Each text has 150 features:

    feature_names = ['F1','F2','F3'...] shape (1, 150)
    features_x = ['0.1','0,765', '0.543'...] shape (700, 150)
    correct_answers_y = ['1','2','4'...] shape (1,700)

I want to use PCA to find out the most informative sets of features, something like:

    Component1 = 0,76*F1+0.11*F4-0.22*F7

How can I do so? The code from sklearn user gide have some numbers as output, but I don`t understand how to unterpret them.

    fit_xy = pca.fit(features_x,correct_answers_y)
    array([  4.01783322e-01,   1.98421989e-01,  3.08468655e-01,
     4.28813755e-02, ...])

Answer 1

Not sure where that array comes from, but it looks like the output of explained_variance_ or explained_variance_ratio_ attributes. They are as they say; explained variance and explained variance ratio compared to your data. Usually when doing a PCA you're defining a minimum of ratio of variance you want to keep from the data.

Lets say you want to keep at least 90% of the variance in your data. Here's code to find how many principle components ( n_components parameter in PCA) you need:

    pca_cumsum = pca.explained_variance_ratio_.cumsum()
    pca_cumsum
    >> np.array([.54, .79, .89, .91, .97, .99, 1])
    np.argmax(pca_cumsum >= 0.9)
    >> 3

And as desertnaut said; labels will be ignored, as it is not used in PCA.