PCA in sklearn
import numpy as np
from sklearn.decomposition import PCA
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
pca = PCA(n_components=2)
pca.fit(X)
print(pca.explained_variance_ratio_)
print(pca.singular_values_)PCA in sklearn
import numpy as np
from sklearn.decomposition import PCA
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
pca = PCA(n_components=2)
pca.fit(X)
print(pca.explained_variance_ratio_)
print(pca.singular_values_)pca python
import numpy as np
from sklearn.decomposition import PCA
pca = PCA(n_components = 3) # Choose number of components
pca.fit(X) # fit on X_train if train/test split applied
print(pca.explained_variance_ratio_)Copyright © 2021 Codeinu
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