I have a data set with more than 20 features, and I applied PCA:
M.fit_transform(all_data) variance = M.explained_variance_ratio_ var = np.cumsum(np.round(M.explained_variance_ratio_, decimals=3)*100) plt.ylabel('% Variance Explained') plt.xlabel('# of Features') plt.title('PCA Analysis') plt.ylim(30,102.5) plt.plot(var, marker="s") plt.show()
var variable, I get
array([ 89., 100., 100., 100., 100., 100., 100., 100., 100., 100.])
I understand this tells us that the variance is explained by 2 features.
So I calculated it again, now the 2 components:
from sklearn.decomposition import PCA M = PCA(n_components = 2) X = M.fit_transform(all_data) plt.scatter(X[:,0],X[:,1])
And this gives a "random looking plot". I understand that the data was changed during the PCA process.
What can I do with this information? How will this help me understand the data?
Is it useful per se? Is it useful as a preparation method for other methods? Which ones can I try?