pca-example.md

PCA 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)

• from sklearn import - import module from lib: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

group: pca

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)

Original: (150, 4)
Reduced:  (150, 3)