python-scikit-learnPCA dimensionality reduction example
from sklearn import decomposition, datasets
X, y = datasets.load_iris(return_X_y=True)
pca = decomposition.PCA(n_components=3)
pca.fit(X)
X = pca.transform(X)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
decomposition.PCA(create PCA dimensionality reduction model | n_componentsreduce to the given number of components (3 in our case) |
.fit(train reduction model model | .transform(transform original data and return reduced dimensions data |
Usage example
from sklearn import decomposition, datasets
X, y = datasets.load_iris(return_X_y=True)
print('Original:', X.shape)
pca = decomposition.PCA(n_components=3)
pca.fit(X)
X = pca.transform(X)
print('Reduced: ', X.shape)output
Original: (150, 4)
Reduced: (150, 3)
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