PCA in a model pipeline
We just saw that legendary Pokemon tend to have higher stats overall. Let's see if we can add a classifier to our pipeline that detects legendary versus non-legendary Pokemon based on the principal components.
The data has been pre-loaded for you and split into training and tests datasets: X_train, X_test, y_train, y_test.
Same goes for all relevant packages and classes(Pipeline(), StandardScaler(), PCA(), RandomForestClassifier()).
Cet exercice fait partie du cours
Dimensionality Reduction in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Build the pipeline
pipe = Pipeline([
('scaler', ____),
('reducer', ____),
('classifier', ____)])