CODEWITHSUNDEEP
pythonmachine-learningscikit-learnpca
Luis Ramon Ramirez Rodriguez
Luis Ramon Ramirez Rodriguez

Reputation: 10383

Using PCA on linear regression

I want to use principal component analysis to reduce some noise before applying linear regression.

I have 1000 samples and 200 features 

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA

X = np.random.rand(1000,200)
y = np.random.rand(1000,1)

With this data I can train my model: 

model.fit(X,y)

But if I try the same after applying PCA

pca = PCA(n_components=8)
pca.fit(X)
PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)
principal_components =  pca.components_

model.fit(principal_components,y)

I get this error:

ValueError: Found input variables with inconsistent numbers of samples: [8, 1000]

Upvotes: 3

Views: 14540

Answers (1)

jtitusj
jtitusj

Reputation: 3086

Try this:

pca = PCA(n_components=8)
X_pca = pca.fit_transform(X)

model.fit(X_pca,y)

That is, you simultaneously fit PCA to X and transform it into (1000, 8) array named X_pca. That's what you should use instead of the pca.components_

Upvotes: 10

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