BaşlayınÜcretsiz Başlayın

Dimension reduction of the fish measurements

In a previous exercise, you saw that 2 was a reasonable choice for the "intrinsic dimension" of the fish measurements. Now use PCA for dimensionality reduction of the fish measurements, retaining only the 2 most important components.

The fish measurements have already been scaled for you, and are available as scaled_samples.

Bu egzersiz

Unsupervised Learning in Python

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • Import PCA from sklearn.decomposition.
  • Create a PCA instance called pca with n_components=2.
  • Use the .fit() method of pca to fit it to the scaled fish measurements scaled_samples.
  • Use the .transform() method of pca to transform the scaled_samples. Assign the result to pca_features.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import PCA
____

# Create a PCA model with 2 components: pca
pca = ____

# Fit the PCA instance to the scaled samples
____

# Transform the scaled samples: pca_features
pca_features = ____

# Print the shape of pca_features
print(pca_features.shape)
Kodu Düzenle ve Çalıştır