Pca python

import numpy as np
from sklearn.decomposition import PCA

pca = PCA(n_components = 3) # Choose number of components
pca.fit(X) # fit on X_train if train/test split applied

print(pca.explained_variance_ratio_)

Pca python

from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)

Pca python

from sklearn.decomposition import PCA

pca = PCA(n_components=3)
pca.fit(features)
features_pca = pca.transform(features)
print("original shape:   ", features.shape)
print("transformed shape:", features_pca.shape)
print(pca.explained_variance_)
print(pca.explained_variance_ratio_)

Scikit learn pca

from sklearn.decomposition import KernelPCA
kpca = KernelPCA(n_components = 2, kernel = 'rbf')
X_train = kpca.fit_transform(X_train)
X_test = kpca.transform(X_test)

Similar codes

0 vote346 views2 answers
Class range python code
Python
0 vote234 views1 answer
Python extract words from string with format
Python
0 vote230 views1 answer
How to add values in python
Python
1 vote237 views1 answer
Python parallel list comprehension
Python
65 votes249 views4 answers
Python csv dict reader
Python
0 vote263 views1 answer
Lucky four codechef solution
Python
0 vote293 views1 answer
Add url parameter in anchor django template
Python Django
0 vote252 views1 answer
Using np.unravel_index on argmax output
Python
0 vote245 views1 answer
Assosciate keys as list to values in python
Python
1 vote262 views1 answer
Plt imshow python
Python