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 + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`decomposition.PCA(` create PCA dimensionality reduction model	`n_components` reduce 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)

Using SVD to reduce dimensions example
How to inverse transform PCA reduced data

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python-scikit-learnPCA dimensionality reduction example

Usage example

output

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