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
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=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_)isinstance in python
x == 1
isinstance(x<3)
#will automatically return true or falseCopyright © 2021 Codeinu
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