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

6 votes104 views3 answers
Messages django
Python
0 vote107 views1 answer
File attributes in os
Python
3 votes103 views4 answers
How to convert comma separated string to list in python
Python
0 vote107 views4 answers
Remove french stopwords with spacy
Python
1 vote123 views2 answers
Make pandas dataframe from elasticsearch
Python
0 vote111 views1 answer
Make a effective figure in python
Python
0 vote120 views2 answers
Isinstance in python
Python
0 vote168 views1 answer
Program in python to print first 10 natural number.
Python
0 vote154 views1 answer
Pandas add days to date
Python
0 vote110 views1 answer
Hash python png
Python