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# python-scikit-learnHow to inverse transform PCA reduced data

``````from sklearn import decomposition, datasets

pca = decomposition.PCA(n_components=3)

pca.fit(X)
reduced = pca.transform(X)
inversed = pca.inverse_transform(reduced)```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 `.inverse_transform(`inverse transform back to original number of dimensions

### Usage example

``````from sklearn import decomposition, datasets

pca = decomposition.PCA(n_components=3)

pca.fit(X)
reduced = pca.transform(X)
inversed = pca.inverse_transform(reduced)

print('Reduced:  ', reduced.shape)
print('Invresed: ', inversed.shape)

print(inversed[0:5])``````
###### output
``````Reduced:   (150, 3)
Invresed:  (150, 4)
[[5.09928623 3.50072335 1.40108561 0.1982949 ]
[4.86875839 3.03166108 1.4475168  0.12536791]
[4.69370023 3.20638436 1.30958161 0.18495067]
[4.6238432  3.07583667 1.46373578 0.25695828]
[5.0193263  3.58041421 1.37060574 0.24616799]]
``````