python-scikit-learnUsing SVD to reduce dimensions example
from sklearn import decomposition, datasets
X, y = datasets.load_iris(return_X_y=True)
svd = decomposition.TruncatedSVD(n_components=2)
svd.fit(X)
X = svd.transform(X)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
.TruncatedSVD(creates dimensionality reduction model based on truncated SVD | .fit(train reduction 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)
svd = decomposition.TruncatedSVD(n_components=2)
svd.fit(X)
X = svd.transform(X)
print('Reduced: ', X.shape)output
Original: (150, 4)
Reduced: (150, 2)
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