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Using PCA

In this exercise, you'll apply PCA to the wine dataset, to see if you can increase the model's accuracy.

Cet exercice fait partie du cours

Preprocessing for Machine Learning in Python

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Instructions

  • Instantiate a PCA object.
  • Define the features (X) and labels (y) from wine, using the labels in the "Type" column.
  • Apply PCA to X_train and X_test, ensuring no data leakage, and store the transformed values as pca_X_train and pca_X_test.
  • Print out the .explained_variance_ratio_ attribute of pca to check how much variance is explained by each component.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Instantiate a PCA object
pca = ____()

# Define the features and labels from the wine dataset
X = wine.drop(____, ____)
y = wine["Type"]

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)

# Apply PCA to the wine dataset X vector
pca_X_train = ___.____(____)
pca_X_test = ___.____(____)

# Look at the percentage of variance explained by the different components
print(____)
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